Hiring Engineers in 2025: Testing AI Collaboration, Not AI Avoidance
Published:
Why Traditional Coding Interviews Are Failing and How to Build Teams for an AI-Native World
Last week, I watched a junior developer use Claude to solve a distributed systems problem that would have sent me into an existential crisis just two years ago. The solution was elegant, well-documented, and production-ready. The developer understood every line of code, could explain the trade-offs, and knew exactly how to modify it for their specific use case.
Yet under our traditional interview process, this same developer might have struggled to implement a binary search algorithm from memory. You know, that skill we’ve all used precisely never in production but somehow remains the gold standard for technical assessment.
The uncomfortable truth: we’re testing yesterday’s skills for tomorrow’s jobs.
Whilst engineers in the real world collaborate daily with AI tools to build better software faster, our interview processes remain stubbornly stuck in 2015. We’re asking candidates to solve algorithmic puzzles without any assistance. We’re essentially hiring people based on their ability to recreate conditions that haven’t existed in actual engineering work since before the pandemic.
It’s rather like hiring a Formula 1 driver based on their ability to change a tyre with a manual jack whilst blindfolded. Impressive? Perhaps. Relevant? Absolutely not.
The Real Solution (Hint: It’s Not Making Things Easier)
The answer isn’t to make interviews easier or lower our standards. Quite the opposite, actually. We need a fundamental shift in how we assess engineering talent:
- Test collaboration with AI tools, not performance despite them
- Evaluate critical thinking about AI-generated solutions
- Assess systematic problem-solving over algorithmic memorisation
- Focus on engineering judgement rather than pattern recognition
But here’s the deeper insight that should keep every CTO awake at night: this shift towards AI-collaborative interviews doesn’t just modernise our hiring process. It exposes fundamental flaws in how we’ve always evaluated engineers.
The rise of AI forces us to confront an uncomfortable question: were we ever really testing the skills that matter most?
The Awakening: What AI Has Exposed About Our Hiring Practices
For decades, technical interviews have centred around algorithmic challenges, whiteboard coding sessions, and puzzle-solving exercises. These approaches felt safe and objective. They provided clear pass/fail criteria and seemed to measure raw intellectual horsepower.
But AI has pulled back the curtain on what we were actually testing versus what we thought we were measuring. Spoiler alert: the results aren’t flattering.
The False Security of “Pure” Coding Tests
Traditional coding interviews gave hiring managers a false sense of security. When a candidate could perfectly implement quicksort or traverse a binary tree, it felt like we were measuring fundamental problem-solving ability. The tests seemed objective, repeatable, and fair. Everyone got the same challenge, and the solutions could be directly compared.
We’ve all been there: watching a brilliant engineer fail to implement fizz buzz under pressure whilst knowing they could architect a global payments system in their sleep.
What were these tests actually measuring? In most cases, they were assessing pattern recognition and memorisation rather than genuine problem-solving capability. Candidates who had practised hundreds of LeetCode problems would outperform brilliant engineers who simply hadn’t spent months drilling algorithmic puzzles. We were rewarding rote learning over creative thinking, preparation over problem-solving ability.
The uncomfortable realisation is that many of our “objective” technical assessments were actually testing a candidate’s ability to perform under artificial constraints that bore little resemblance to real engineering work.
Quick reality check:
- When did you last implement a hash table from scratch in production?
- When did you last solve a complex system design problem without access to documentation, Stack Overflow, or colleague input?
- When did you last debug a production issue by implementing a perfect binary search algorithm?
The Skills That Actually Matter
Whilst we were focused on algorithmic gymnastics, we were missing the competencies that truly distinguish exceptional engineers from average ones. The skills that matter most in real engineering roles are fundamentally different from what traditional interviews assessed.
Great engineers excel at:
- Breaking down ambiguous, complex problems into manageable pieces
- Working effectively with incomplete requirements and changing constraints
- Communicating technical concepts clearly to both technical and non-technical stakeholders
- Making thoughtful architectural decisions when faced with multiple viable options
- Adapting quickly to new technologies and frameworks
- Debugging systematically when things go wrong
- Considering the broader system impact of their decisions
- Balancing technical debt against feature delivery
- Collaborating effectively with team members who have different expertise
None of these crucial abilities were effectively tested by asking someone to implement a function to find the longest palindromic substring. Yet these are the skills that determine whether an engineer will thrive in your organisation and contribute meaningfully to your products.
AI as the Catalyst for Change
The introduction of AI tools into software development hasn’t just changed how we write code. It’s forced us to confront the inadequacy of our hiring practices. When AI can generate the solution to most traditional coding interview questions in seconds, we can no longer pretend these tests measure what we need them to measure.
But this disruption is actually an opportunity. AI forces us to focus on uniquely human engineering skills: critical thinking, system design, requirements analysis, and the ability to work effectively with ambiguous or conflicting information. These capabilities become more valuable, not less, in an AI-augmented world.
The irony: AI helps us identify what humans do best. While AI excels at pattern matching and code generation, humans excel at understanding context, making judgement calls, and navigating the messy complexity of real-world systems. The engineers who will thrive in an AI-native world are those who can leverage AI’s strengths whilst contributing uniquely human insights.
Beyond the Algorithm: What We Should Really Be Testing
The shift from traditional coding challenges to AI-collaborative assessments represents more than just a technological update. It’s a fundamental reimagining of what engineering competency means in 2025 and beyond.
Think of it this way: we’ve spent years testing whether candidates can recite Shakespeare from memory when what we actually need is someone who can direct a brilliant performance.
The Shift from “What” to “How”
Instead of testing whether candidates know specific algorithms or can recall particular syntax, we should be evaluating how they approach problems, how they think through solutions, and how they work when faced with uncertainty.
Traditional interview focus:
- Can you implement binary search?
- Do you remember the syntax for array manipulation?
- Can you solve this puzzle under time pressure?
What we should actually care about:
- How do you break down complex problems?
- What questions do you ask when information is missing?
- How do you handle ambiguity in specifications?
- Do you start by understanding requirements fully, or jump straight to implementation?
Systematic problem-solving becomes far more valuable than memorised solutions. A candidate who can methodically work through an unfamiliar challenge, even if they don’t immediately know the answer, is often more valuable than someone who can quickly implement a well-known algorithm but struggles with novel situations.
Tolerance for uncertainty is crucial in real engineering work. Requirements change, systems behave unexpectedly, and new technologies emerge constantly. Engineers who can work effectively despite incomplete information and evolving constraints are invaluable team members.
Core Competencies for AI-Collaborative Engineering
The rise of AI tools creates entirely new categories of engineering competency that we need to assess. These skills didn’t exist in traditional software development but are now fundamental to effective engineering work.
Competency | What It Means | Why It Matters |
---|---|---|
Critical Evaluation | Spotting flaws in AI-generated solutions | AI can produce plausible but incorrect code |
Prompt Mastery | Effectively directing AI tools | Getting quality output requires clear communication |
Integration Skills | Weaving AI output into existing systems | Code must fit into established architectures |
Strategic Judgement | Knowing when AI helps vs hinders | Some problems need human insight |
Quality Assurance | Maintaining standards regardless of source | Good code is good code, wherever it comes from |
Critical Evaluation represents perhaps the most important new competency. Engineers must be able to assess AI-generated solutions for correctness, efficiency, maintainability, and appropriateness for the specific context. This requires not just technical knowledge but also the ability to spot edge cases, security vulnerabilities, and potential integration issues that AI might miss.
Prompt Mastery involves effectively directing AI tools to achieve specific outcomes. This isn’t just about writing clear instructions. It’s about understanding how to iterate on prompts, how to provide appropriate context, and how to guide AI towards solutions that meet both functional and non-functional requirements.
Integration Skills encompass the ability to take AI-generated code and incorporate it thoughtfully into existing systems. This requires understanding how new code fits into established architectures, how it affects testing strategies, and how it impacts system performance and maintainability.
Strategic Judgement involves knowing when AI tools are helpful versus when they might be counterproductive. Some problems are better solved through human insight, collaborative discussion, or established patterns rather than AI generation. Engineers need to develop intuition about when to reach for AI tools and when to rely on other approaches.
Quality Assurance means maintaining engineering standards regardless of how code is generated. Whether code comes from AI, junior developers, or senior architects, it must meet the same standards for readability, testability, security, and performance. Engineers need to apply consistent quality criteria across all code sources.
The Meta-Skill: Knowing When NOT to Use AI
Perhaps counterintuitively, one of the most important skills in an AI-native world is recognising when AI tools shouldn’t be used. This meta-skill requires deep understanding of both AI capabilities and engineering principles.
When AI might not be the answer:
- Sensitive data or security considerations that make AI usage inappropriate
- Problems requiring deep domain expertise that current AI tools cannot provide
- Learning opportunities where working through challenges manually builds important skills
- Creative problem-solving that benefits from human intuition and experience
- Complex system interactions that AI might not fully understand
Engineers also need to understand the limitations of current AI capabilities. While AI excels at code generation and pattern recognition, it can struggle with complex system interactions, business logic that requires deep domain knowledge, and edge cases that weren’t well-represented in training data.
Finally, there’s the important consideration of skill development. Over-reliance on AI for certain types of problems can prevent engineers from developing crucial problem-solving abilities and deep technical understanding. Experienced engineers need judgement about when AI assistance enhances their work versus when it might hinder their professional growth.
The bottom line: The best AI-collaborative engineers aren’t those who use AI for everything. They’re those who use it strategically, critically, and appropriately.
Embracing AI in the Interview Process
Before we can effectively assess AI-collaborative skills, we need to create interview environments where candidates can actually use AI tools. This requires addressing practical, technical, and cultural challenges that many organisations haven’t yet considered.
Let’s be honest: most companies are still figuring out how to let their own employees use AI tools properly, let alone candidates in high-stakes interviews.
Making AI Use Transparent and Fair
The key to successful AI-inclusive interviews is transparency. Candidates should share their entire screen during any coding portions of the interview, making their process visible to interviewers. This isn’t about surveillance (though it might feel a bit like being watched whilst cooking dinner for the first time).
What this looks like in practice:
Do This | Not This |
---|---|
“Please share your entire screen so we can see your problem-solving process” | “We’ll be monitoring your activity” |
“We’re interested in how you work with AI tools” | “Don’t let us catch you using AI” |
“Use whatever tools you normally would” | “Only use approved software” |
Establish clear boundaries upfront about what you’re observing versus what you’re assessing. You’re watching their problem-solving process, how they interact with AI tools, and how they validate AI-generated solutions. You’re not judging their choice of tools, evaluating their personal productivity setup, or making assumptions about their AI dependency.
Privacy assurances are crucial for creating psychological safety. Candidates need to know that you’re focused on their technical approach, not scrutinising their personal information, browsing history, or private communications. Consider providing dedicated interview environments or clear guidelines about which applications should be visible.
Create psychological safety around tool usage. Many candidates may feel uncertain about whether AI use is truly acceptable, leading them to avoid tools they would normally rely on. Make it explicitly clear that AI tool usage is not just permitted but expected, and that you’re interested in seeing their authentic problem-solving approach.
Practical Implementation
Getting the technical setup right can make or break these interviews. Nobody wants to spend the first 15 minutes of an interview troubleshooting screen sharing whilst the candidate’s anxiety levels skyrocket.
Technical Setup Checklist:
For remote interviews:
- Ensure candidates have reliable screen sharing capabilities
- Verify they can access their preferred AI tools
- Test audio/video quality beforehand
- Have a backup communication method ready
For in-person interviews:
- Provide laptops with AI tools already configured, or
- Allow candidates to use their own devices with appropriate network access
- Ensure reliable internet connectivity
- Have technical support available
Ground Rules Framework:
- Opening explanation: “AI tool usage is encouraged in this interview”
- Clarify observations: “We’ll be watching your problem-solving process”
- Set expectations: “Think out loud as you work”
- Address concerns: “Any questions about the setup before we begin?”
Interviewer Training Requirements:
Your interview team will need new skills to observe process rather than judge tool choices. Traditional coding assessment criteria won’t work here. Interviewers need to recognise:
- Effective AI collaboration patterns
- Good prompting strategies
- Appropriate scepticism about automated suggestions
- Quality assessment of AI-generated solutions
Documentation Standards:
Create evaluation rubrics that focus on AI-collaborative competencies rather than traditional coding metrics. This ensures fair assessment regardless of which specific AI tools candidates prefer or how familiar interviewers are with particular platforms.
Addressing Common Concerns
“What if they become dependent on AI tools?”
This concern misses the fundamental point. In real engineering work, effective AI collaboration is a strength, not a weakness. We should be assessing candidates’ ability to leverage all available tools effectively, not their ability to work with artificial constraints.
It’s like worrying that a carpenter is “dependent” on power tools. The question isn’t whether they can build a house with hand tools (though that might be impressive), but whether they can build a better house faster with modern equipment.
“How do we ensure fairness?”
Create level playing fields where all candidates have access to similar AI capabilities. This might mean:
- Providing standard AI tool access during interviews
- Allowing candidates to use their preferred tools whilst ensuring everyone has equivalent functionality
- Testing the setup beforehand to avoid technical difficulties
- Having backup plans for when technology fails
“What about security concerns?”
Security concerns can be addressed through practical measures appropriate to your environment:
- For high-security organisations: Provide isolated interview environments with approved AI tools
- For initial screening: Conduct early interviews in less sensitive contexts before bringing candidates on-site
- For general concerns: Use standard security practices (VPNs, approved devices, monitored environments)
The goal is finding practical solutions that maintain security without eliminating the assessment of real-world skills.
“Won’t this make interviews take longer?”
Actually, AI-collaborative interviews can be more efficient than traditional ones. Candidates spend less time struggling with syntax and more time demonstrating problem-solving approaches. You get to see their authentic working style rather than their ability to perform under artificial pressure.
Plus, you’re testing skills they’ll actually use on the job, which tends to correlate better with eventual performance than algorithm memorisation contests.
A Practical Example: The Code Review Challenge
To illustrate how AI-collaborative interviews work in practice, let me walk through a detailed scenario that I’ve been developing as part of a comprehensive assessment framework. This example demonstrates how to test fundamental engineering principles whilst embracing AI tool usage.
Think of this as the difference between asking someone to recite a recipe from memory versus watching them actually cook a meal. Both involve food, but only one tells you whether they can feed people.
The Scenario Setup
Present candidates with a piece of AI-generated code that solves a realistic business problem but violates several core engineering principles. Here’s the setup:
“Our development team has been experimenting with AI pair programming for a new user registration feature. The AI generated this solution, and whilst it works in our test environment, we need to prepare it for production. I’d like you to review this code and walk me through your assessment and approach to improving it.”
Example Code Structure:
1class UserRegistrationService:
2 def register_user(self, email, password, name):
3 # 200 lines of mixed responsibilities:
4 # - User authentication logic
5 # - Email notification sending
6 # - Activity logging
7 # - Database updates across multiple tables
8 # - Report generation
9 # - Business rule validation
10 # (All in one massive method with no clear testing strategy)
The Testing Nightmare:
1def test_user_registration():
2 # How do you test this monolith?
3 # - Need a real database for user creation
4 # - Need email service running for notifications
5 # - Need logging infrastructure for audit trails
6 # - Need reporting system for analytics
7 # - Any failure breaks everything
8 # - No way to test individual concerns in isolation
This scenario feels realistic because it mirrors actual situations where AI tools generate functional but imperfect code. It’s not an artificial puzzle. It’s the kind of code review that happens regularly in modern development teams, especially when testing wasn’t considered during initial development.
Why this works:
- Candidates recognise the situation immediately
- The code “works” so they can’t dismiss it outright
- Multiple improvement angles exist (no single “right” answer)
- It tests both technical knowledge and real-world engineering practices
- The testing challenges are immediately obvious to experienced engineers
What You’re Really Testing
This scenario assesses multiple levels of engineering competency simultaneously, like a good medical exam that checks several systems at once.
Level 1: Fundamental Design Principles
- Can they identify single responsibility principle violations?
- Do they recognise tight coupling between unrelated concerns?
- Can they spot missing error handling for production use?
- Do they immediately think about testability when reviewing code?
Level 2: Testing Strategy and TDD Thinking
- Do they recognise that the current structure makes comprehensive testing nearly impossible?
- Can they articulate why testing this monolith requires excessive setup and infrastructure?
- Do they understand how separation of concerns enables isolated unit testing?
- Can they explain how proper design makes testing easier, not harder?
Level 3: Domain-Driven Design Thinking
- Do they identify implicit domain boundaries hidden within the monolithic service?
- Can they articulate how user management, notification handling, and analytics represent different business concerns?
- Do they understand the concept of bounded contexts?
- Do they consider how each domain boundary should have its own testing strategy?
Level 4: Architectural Pattern Recognition
- Do they recognise opportunities to apply established patterns (Command, Factory, Observer)?
- Can they suggest appropriate refactoring strategies that improve maintainability without breaking existing functionality?
- Do they consider alternatives and trade-offs?
- Do they think about how patterns like Dependency Injection enable better testing?
Level 5: System Impact and Testing Considerations When candidates propose changes, do they think about:
- Effects on existing code and integration points
- Database performance implications
- How to maintain test coverage during refactoring
- Testing strategy for new architecture (unit, integration, end-to-end)
- Deployment complexity considerations
- How to test in production-like environments
Level 6: Communication and Collaboration
- Can they clearly articulate technical problems and solutions?
- Do they explain their reasoning in ways that would be helpful to team members with different experience levels?
- Can they handle follow-up questions and alternative suggestions?
- Can they explain the business value of good testing practices to non-technical stakeholders?
The AI Collaboration Layer
The most interesting aspects of this assessment emerge when candidates use AI tools to assist with their analysis and proposed solutions. This reveals entirely new dimensions of engineering competency that traditional interviews couldn’t assess.
Effective AI Collaboration Patterns:
Good Signs | Red Flags |
---|---|
Asks AI to help design testable architecture | Accepts AI code without considering testability |
Requests AI assistance with test strategy planning | Assumes AI-generated code is automatically testable |
Uses AI to generate comprehensive test scenarios | Relies on AI for testing without validation |
Validates AI suggestions against testing best practices | Ignores testing implications of AI suggestions |
Iterates with AI to improve test coverage | Stops at functional code without test considerations |
Watch for These Behaviours:
- Testing-aware prompting: Do they ask AI to help design code that’s inherently testable? For example: “Help me refactor this user registration code into testable components with clear dependencies.”
- Test strategy collaboration: Do they use AI to brainstorm testing approaches? “What testing strategy would work best for a user registration system with email notifications and audit logging?”
- Mock and stub awareness: When AI suggests solutions, do they consider how external dependencies can be mocked or stubbed for testing? Do they understand the testability implications of different architectural choices?
- TDD mindset: Do they think about tests first when considering refactoring approaches? “How would we write tests for this proposed architecture before implementing it?”
- Edge case generation: Can they use AI to help identify edge cases and testing scenarios they might not have considered? “What edge cases should I test for email validation in user registration?”
What Good Looks Like
Strong candidates approach this scenario systematically rather than jumping immediately to solutions. Here’s the pattern we typically see from engineers with solid testing practices:
Phase 1: Understanding and Testing Assessment (5-10 minutes)
- Ask clarifying questions about business requirements
- Immediately identify that the current code is virtually untestable
- Question existing test coverage and testing strategy
- Understand system context and existing architectural patterns
- Ask about current testing practices and CI/CD pipeline
Phase 2: Analysis with Testing Focus (10-15 minutes)
- Demonstrate clear thinking about domain boundaries and separation of concerns
- Explain how current structure makes testing expensive and unreliable
- Articulate the testing benefits of proper separation of concerns
- Identify which parts need unit tests vs integration tests vs end-to-end tests
- Consider performance implications and testing strategies for each concern
Phase 3: Solution Design with TDD Approach (15-20 minutes)
- Propose starting with test cases to define expected behaviour
- Use AI to help design testable interfaces and dependency injection
- Consider how each proposed component can be tested in isolation
- Validate proposed solutions against both business requirements and testability
- Plan for test doubles, mocks, and stubs where appropriate
Phase 4: Implementation and Testing Strategy (5-10 minutes)
- Design a comprehensive testing strategy (unit, integration, end-to-end)
- Consider deployment considerations and testing in production-like environments
- Plan for maintaining test coverage during incremental refactoring
- Discuss testing tools and frameworks that would support the new architecture
- Plan for rollback scenarios and how testing supports safe deployments
Throughout: Testing-First Mindset
- Consistently consider “how would I test this?” when evaluating solutions
- Understand that good design and good testing practices reinforce each other
- Think about the long-term maintainability benefits of comprehensive testing
Red Flags to Watch For
Several warning signs indicate candidates who may struggle with AI-collaborative engineering work, particularly around testing practices:
Technical Red Flags:
- Missing fundamental design principle violations (suggests gaps in engineering knowledge)
- Not recognising the testing implications of poor architectural choices
- Accepting solutions without considering how they’ll be tested
- Over-engineering solutions without considering implementation and testing complexity
Testing-Specific Red Flags:
- Not immediately thinking about testability when reviewing code
- Suggesting refactoring approaches without considering test coverage maintenance
- Not understanding the relationship between good design and testable code
- Treating testing as an afterthought rather than a design consideration
- Not recognising when AI suggestions make code harder to test
Process Red Flags:
- Jumping to solutions without understanding the problem
- Failing to ask questions about system context or business requirements
- Not asking about existing testing practices or coverage
- Inability to work systematically through complex problems
AI Collaboration Red Flags:
- Using AI as a replacement for thinking rather than a tool to enhance thinking
- Not considering testing when using AI to generate code solutions
- Accepting AI code without validating its testability
- Failing to iterate and improve on initial AI suggestions
- Not using AI to help with test case generation and edge case identification
The goal isn’t to catch people out. It’s to identify candidates who can work effectively in an AI-augmented environment whilst maintaining high engineering standards, including the discipline of comprehensive testing that makes software reliable and maintainable.
Other Assessment Approaches That Work
The code review challenge is just one tool in a comprehensive AI-collaborative interview framework. Effective assessment requires multiple scenarios that test different aspects of engineering competency, much like a well-balanced fitness test rather than just checking whether someone can lift heavy weights.
After all, you wouldn’t hire a chef based solely on their ability to julienne carrots whilst blindfolded, no matter how impressive their knife skills.
The Assessment Arsenal
I’ve been developing several complementary assessment approaches that work together to provide a complete picture of AI-collaborative competency:
-
The Ambiguous Requirements Challenge
This scenario presents candidates with deliberately vague problem statements that mirror real-world situations: “Our customer support team reports that users are complaining about slow performance, and it’s affecting user satisfaction scores.” You know, the kind of delightfully unhelpful brief that makes every engineer’s eye twitch slightly. The focus is on systematic investigation skills and how candidates collaborate with AI tools to structure complex problem analysis.
-
Legacy Code Archaeology
Rather than pristine algorithms, candidates work with realistic, messy production code that’s accumulated technical debt over time. Think spaghetti code with a PhD in complexity theory. This reveals code comprehension abilities, debugging skills, and how they handle the reality that 90% of engineering work involves improving existing systems rather than building from scratch.
-
The Trade-off Discussion
Candidates evaluate technical decisions with multiple viable approaches: authentication strategies, database choices, or architecture patterns. This tests systematic decision-making, research abilities, and how they use AI tools for analysis whilst maintaining critical judgement about context-specific factors. No “just use MongoDB” responses allowed.
-
Collaborative Debugging Sessions
Present realistic bug reports with logs, monitoring data, and deployment notes. The goal isn’t necessarily solving the problem completely but observing systematic debugging methodology and how candidates collaborate with AI tools during investigation and hypothesis formation. Bonus points if they don’t immediately blame the frontend team.
-
Testing Strategy Design
Given a feature specification, candidates design comprehensive testing approaches. This reveals quality assurance thinking, understanding of different testing types, and how they leverage AI tools for test case generation whilst maintaining coverage and reliability standards. Because “it works on my machine” isn’t a testing strategy.
Why Multiple Approaches Matter
Different scenarios reveal different strengths. A candidate might excel at architectural thinking but struggle with ambiguous requirements. Another might be brilliant at debugging but poor at systematic testing design. It’s like discovering that your brilliant chess player can’t play poker to save their life.
Key benefits of comprehensive assessment:
- Competency coverage: Real engineering involves diverse challenges requiring different skills
- Realistic skill distribution: No engineer is equally strong at everything (despite what their CVs claim)
- AI collaboration variety: Effective AI partnership looks different across various engineering tasks
- Performance prediction: Better correlation with actual job success than single-dimension testing
The Framework Approach
-
Progressive Complexity Assessment
Start with foundational AI collaboration skills and progress to more complex scenarios. This helps distinguish between candidates who can handle basic tool usage versus those ready for senior engineering challenges requiring sophisticated AI partnership. It’s the difference between someone who can use a calculator and someone who can conduct a symphony orchestra.
-
Competency Mapping
Each assessment maps to specific competencies required for the role:
- Systematic thinking and problem decomposition
- Critical evaluation of AI-generated solutions
- Communication effectiveness under uncertainty
- Quality assurance mindset and testing discipline
- Adaptability to new tools and methodologies
-
Real-World Alignment
- Every scenario reflects actual engineering work rather than artificial puzzles. The goal is predicting job performance, not testing abstract problem-solving abilities disconnected from daily engineering challenges. We’re hiring engineers, not puzzle enthusiasts.
Implementation Considerations
-
Scenario Selection and Calibration
Choose assessment approaches based on role requirements and organisational context. Senior positions might emphasise architectural decision-making, whilst junior roles focus more on systematic problem-solving and code comprehension. One size fits all is the enemy of effective assessment.
-
Interviewer Training Requirements
Each assessment type requires specific observation skills and evaluation criteria. Interviewers need training in:
- Recognising effective AI collaboration patterns
- Evaluating thinking processes over outcomes
- Distinguishing sophisticated AI partnership from surface-level tool usage
- Managing the psychological safety needed for authentic performance
-
Evaluation Framework Development
Traditional coding rubrics don’t apply to these scenarios. Success requires developing new evaluation criteria focused on collaboration effectiveness, critical thinking, and quality judgement rather than syntax correctness or algorithmic efficiency.
The Comprehensive Advantage
Organisations that develop sophisticated, multi-faceted approaches to AI-collaborative assessment gain significant advantages in talent acquisition. They can identify candidates who truly excel at leveraging AI tools whilst maintaining engineering discipline and quality standards.
The investment in developing these assessment approaches pays dividends through better hiring outcomes, reduced mis-hires, and teams that can effectively navigate an AI-augmented engineering landscape. Plus, you’ll actually enjoy conducting interviews again instead of watching yet another candidate struggle through implementing a binary search tree.
The complete framework I’ve developed includes detailed scenario specifications, evaluation criteria, interviewer training materials, and implementation guidance for organisations serious about modernising their technical hiring process.
Implementing This Approach in Your Organisation
Transitioning to AI-collaborative interviews requires careful planning and change management. Most organisations will need to address both technical and cultural challenges whilst building new competencies within their interview teams.
Getting Buy-in from Hiring Managers
The biggest obstacle to implementing AI-collaborative interviews is often internal resistance from hiring managers who worry about “lowering standards” or compromising assessment quality. Address these concerns by demonstrating the correlation between AI-collaborative skills and actual job performance.
Start by piloting the approach with a small number of interviews and gathering data on both candidate performance and interviewer feedback. Document specific examples where traditional interviews might have missed strong candidates or where AI-collaborative assessments revealed important competencies.
Emphasise that this approach raises standards rather than lowering them. AI-collaborative interviews test more sophisticated skills and provide better predictors of actual job performance than traditional algorithmic challenges.
Consider starting with pilot programmes for specific roles or teams where the benefits are most obvious, then expanding based on demonstrated success.
Training Your Interview Team
Your existing interview team will need new skills to effectively assess AI-collaborative competencies. This represents a significant investment but is essential for consistent, fair evaluation.
Train interviewers to observe process rather than just outcomes. They need to recognise effective AI collaboration, identify good prompting strategies, and assess critical thinking about AI-generated solutions. This requires moving beyond traditional coding assessment criteria.
Develop observation skills for recognising systematic problem-solving approaches, effective communication during technical work, and appropriate scepticism about automated suggestions. These skills differ significantly from traditional interview evaluation techniques.
Create consistent evaluation frameworks that focus on AI-collaborative competencies. Develop rubrics that help interviewers assess these new skill areas fairly and consistently across different candidates and interview sessions.
Balancing Structure with Flexibility
AI-collaborative interviews require careful balance between providing consistent evaluation criteria and allowing for diverse problem-solving approaches. Different candidates may use different AI tools, have different prompting styles, and approach problems from different angles.
Maintain fairness by focusing on underlying competencies rather than specific tool choices or implementation approaches. Create evaluation criteria that assess thinking quality and collaboration effectiveness regardless of which particular AI platforms candidates prefer.
Document decision-making processes to ensure consistency and enable continuous improvement. Keep detailed records of successful hires and their interview performance to refine your assessment criteria over time.
Continuously improve your approach based on hire success rates and feedback from both candidates and interviewers. AI-collaborative interviewing is still an emerging field, so organisations need to remain flexible and adaptive as best practices evolve.ness by focusing on underlying competencies rather than specific tool choices or implementation approaches. Create evaluation criteria that assess thinking quality and collaboration effectiveness regardless of which particular AI platforms candidates prefer.
Document decision-making processes to ensure consistency and enable continuous improvement. Keep detailed records of successful hires and their interview performance to refine your assessment criteria over time.
Continuously improve your approach based on hire success rates and feedback from both candidates and interviewers. AI-collaborative interviewing is still an emerging field, so organisations need to remain flexible and adaptive as best practices evolve.
The Broader Implications
The shift towards AI-collaborative interviews represents more than just a hiring process update. It signals a fundamental transformation in how we think about engineering competency, team building, and competitive advantage in the software industry.
It’s rather like the moment when Formula 1 teams realised that aerodynamics mattered more than raw horsepower. The rules of the game have changed, and those who adapt fastest will leave the competition behind.
Cultural Shift Required
From Gatekeeping to Enablement
Traditional interviews often function as elaborate gatekeeping mechanisms, designed to filter out candidates who might struggle under artificial constraints. The underlying assumption is scarcity: we must be highly selective because good engineers are rare and expensive mistakes are costly.
AI-collaborative interviews represent a philosophical shift towards enablement. Instead of testing whether candidates can perform despite limitations, we’re assessing how effectively they can leverage available tools to deliver exceptional results. The assumption becomes abundance: with the right tools and approach, more people can contribute meaningfully to engineering teams.
This cultural shift affects everything from job descriptions (which skills we emphasise) to performance reviews (how we measure success) to career development (what growth paths we create). Teams that make this transition successfully often find themselves more innovative, productive, and attractive to top talent.
Recognising AI as Infrastructure, Not Competition
The most successful organisations will be those that treat AI tools as fundamental infrastructure rather than optional enhancements or competitive threats. Just as we don’t consider engineers inferior for using IDEs, version control systems, or cloud platforms, effective AI collaboration should become an expected competency rather than a special skill.
This normalisation process requires deliberate culture change:
- Leadership messaging: Senior engineers and managers need to model effective AI usage
- Learning opportunities: Provide training and experimentation time for AI tool adoption
- Success metrics: Include AI collaboration effectiveness in performance evaluations
- Tool investment: Provide access to high-quality AI platforms as standard infrastructure
Developing Existing Teams for the AI-Native Future
The Training and Development Challenge
Whilst hiring AI-collaborative engineers is crucial, most organisations also need to develop their existing teams’ capabilities. This represents a significant training and mentoring challenge that goes far beyond simply providing access to AI tools.
Effective AI collaboration requires developing entirely new competencies: strategic prompting, critical evaluation of generated solutions, quality assurance for AI-assisted development, and architectural thinking that leverages AI capabilities. These skills don’t emerge naturally from traditional engineering experience.
Engineering Leadership Development
Engineering leaders face particularly complex challenges in an AI-native world. They need to guide teams through tool adoption, establish quality standards for AI-generated code, make strategic decisions about AI investment, and mentor engineers in developing collaborative AI skills. Many technical leaders find themselves navigating this transformation without clear frameworks or proven approaches.
The most effective development programmes combine hands-on technical training with leadership coaching around change management, team development, and strategic AI adoption. This requires trainers who understand both the technical and organisational aspects of AI transformation.
Practical Skill Development Approaches
Structured Learning Programmes: Rather than hoping engineers will figure out AI collaboration independently, successful organisations implement systematic training that covers prompting strategies, quality evaluation techniques, and integration best practices.
Mentoring and Coaching: Pairing experienced AI-collaborative engineers with those developing these skills accelerates learning and helps avoid common pitfalls. This mentoring process requires specific frameworks and approaches rather than informal knowledge transfer.
Team-Based Learning: The most effective AI collaboration skills often emerge through team-based problem-solving and peer learning. Facilitating these collaborative learning experiences requires understanding group dynamics and adult learning principles.
Leadership Coaching for Transformation: Engineering leaders need coaching through the cultural and strategic aspects of AI adoption, not just technical training. This involves developing new performance management approaches, team building strategies, and quality assurance frameworks.
Competitive Advantage in Talent Acquisition
Attracting the Right Engineers
Engineers who are excited about working with cutting-edge tools and methodologies will be drawn to organisations that demonstrate sophisticated understanding of AI-collaborative work. Conversely, engineers who prefer traditional constraint-based approaches may self-select out of the process.
This creates a virtuous cycle: better AI-collaborative hiring practices attract engineers who excel at AI collaboration, leading to more effective teams and better products, which in turn attracts even stronger candidates.
Speed and Quality of Assessment
AI-collaborative interviews can actually be more efficient than traditional approaches. Candidates spend less time struggling with syntax or obscure algorithmic details and more time demonstrating real problem-solving capabilities. Interviewers gain better insights into actual job-relevant skills in less time.
Employer Branding Benefits
Companies that implement thoughtful AI-collaborative hiring processes send strong signals about their technical sophistication and forward-thinking approach. This becomes increasingly important as competition for top engineering talent intensifies.
Team Composition and Dynamics
Complementary AI-Collaborative Skills
Different engineers will excel at different aspects of AI collaboration. Some might be exceptional at architectural design with AI assistance, whilst others excel at quality assurance or debugging with AI tools. Building teams with complementary AI-collaborative strengths becomes a key strategic consideration.
Faster Onboarding and Productivity
Engineers hired through AI-collaborative processes often achieve productivity faster because they’re already comfortable with the tools and approaches your team uses daily. There’s less disconnect between interview conditions and actual work conditions.
Knowledge Sharing and Collaboration Patterns
Teams with strong AI-collaborative skills often develop more effective knowledge sharing practices. When engineers are comfortable using AI tools for research, documentation, and problem-solving, they can help teammates more effectively and tackle complex challenges collaboratively.
The Evolution of Engineering Roles
What “Senior” Means in an AI-Native World
Traditional markers of seniority (algorithm knowledge, syntax memorisation, years of experience with specific technologies) become less relevant. New markers emerge:
- Quality judgement: Ability to evaluate and improve AI-generated solutions
- Strategic thinking: Understanding when and how to apply AI tools effectively
- System design: Architecting solutions that work well with AI assistance
- Mentoring capability: Helping others develop effective AI collaboration skills
New Career Development Paths
Engineering career progression increasingly involves developing sophisticated AI collaboration capabilities rather than just accumulating technology-specific experience. This creates new opportunities for rapid career growth based on AI-collaborative competency rather than traditional tenure-based advancement.
Continuous Learning as Core Competency
The rapid evolution of AI tools makes continuous learning more critical than ever. Engineers who can quickly adapt to new AI capabilities and integrate them effectively into their workflows become increasingly valuable regardless of their experience with specific technologies.
Industry-Wide Transformation
Productivity and Innovation Acceleration
Teams that excel at AI collaboration can deliver software faster and with higher quality than traditional teams. This creates competitive pressure for industry-wide adoption of AI-collaborative practices and the hiring approaches that identify these capabilities.
Democratisation of Complex Engineering
Effective AI collaboration makes sophisticated engineering techniques more accessible to a broader range of engineers. This potentially expands the talent pool whilst raising the overall quality bar for engineering work.
Quality and Reliability Evolution
As AI tools become more sophisticated, the engineering challenges shift from implementation details to higher-level design, quality assurance, and system architecture. This evolution requires engineers with strong critical thinking and system design capabilities rather than just coding proficiency.
The Strategic Imperative
Organisations that successfully transition to AI-collaborative hiring and team building will gain substantial advantages in an increasingly competitive market. They’ll build more effective engineering teams, deliver better products faster, and attract top talent more effectively.
Those that cling to traditional hiring approaches risk building teams optimised for a world that no longer exists. They’ll struggle to compete with organisations that have embraced AI-collaborative engineering effectively.
The window for making this transition strategically rather than reactively is closing. The companies that act now will shape the future of engineering team building. Those that wait will find themselves playing catch-up in an increasingly AI-native industry.
Conclusion
The integration of AI tools into software development has created an urgent need to modernise our approach to technical hiring. Traditional algorithmic interviews that ban AI assistance are not just outdated—they’re actively counterproductive, testing skills that are no longer relevant whilst missing the competencies that actually determine engineering success.
But here’s what makes this moment so crucial: we’re not just talking about updating interview questions. We’re witnessing a fundamental shift in what it means to be an exceptional engineer. The companies that recognise this transformation and act decisively will build the teams that define the future of software development.
The Stakes Have Never Been Higher
Companies that continue using outdated assessment methods will struggle to identify and attract engineers who can thrive in an AI-augmented world. They’ll miss talented candidates who excel at AI collaboration whilst potentially hiring engineers who can solve abstract puzzles but struggle with real-world problem-solving using modern tools.
Meanwhile, organisations that successfully transition to AI-collaborative interviews will build more effective engineering teams, achieve better hiring outcomes, and position themselves at the forefront of industry transformation. They’ll attract engineers who are excited about working with cutting-edge tools rather than those who prefer artificial constraints.
The gap between these two approaches will only widen as AI tools become more sophisticated and AI-collaborative skills become more critical for engineering success.
The Path Forward Requires Action, Not Just Understanding
Understanding the need for change is the easy part. The challenge lies in implementation: developing comprehensive assessment frameworks, training interview teams in new evaluation techniques, creating psychological safety for authentic AI collaboration, and building organisational cultures that embrace AI as infrastructure rather than threat.
Start with experimentation. Incorporate AI-collaborative elements into your next few interviews. Observe what you learn about candidates that traditional methods might have missed. Gather feedback from both interviewers and candidates about the experience.
Build systematic approaches. Successful AI-collaborative interviewing requires more than just allowing AI tool usage. It demands new evaluation criteria, interviewer training, and assessment frameworks designed specifically for this new reality.
Invest in team development. Hiring AI-collaborative engineers is only part of the equation. Your existing teams need training, mentoring, and coaching to develop these crucial capabilities. The organisations that excel at both hiring and developing AI-collaborative talent will have the strongest competitive advantages.
The Transformation is Already Underway
Whether your organisation chooses to lead this transformation or follow reluctantly, the shift towards AI-collaborative engineering is happening. The only question is whether you’ll be among the companies that shape this future or those that scramble to catch up.
This transformation won’t happen overnight, and every organisation will need to adapt these principles to their specific context, culture, and technical requirements. But the companies that begin this journey now will have significant advantages in attracting and identifying the engineering talent that drives success in an AI-native world.
The window for making this transition strategically rather than reactively is narrowing. The companies that act now will build the teams that create tomorrow’s software. Those that wait will find themselves competing for yesterday’s engineers to solve tomorrow’s problems.
The future belongs to organisations that can identify, hire, and develop engineers who don’t just use AI tools, but collaborate with them strategically, critically, and effectively.
As I continue developing comprehensive frameworks for AI-collaborative assessment and team development, I’m convinced that this work represents one of the most important shifts in engineering team building we’ll see in our careers. The organisations that embrace this transformation will shape the future of software development.
The question isn’t whether this change will happen. The question is whether your organisation will lead it.
For engineering leaders ready to explore AI-collaborative hiring frameworks and team development strategies in depth, I’d welcome a conversation about your specific challenges and objectives. The transformation to AI-native engineering practices requires both strategic thinking and practical implementation guidance.
About the Author
Tim Huegdon is the founder of Wyrd Technology, a consultancy that helps engineering leaders modernise their hiring practices and build AI-collaborative teams. He specialises in developing assessment frameworks that identify engineers who thrive in AI-augmented environments and provides leadership coaching to help organisations transition from traditional algorithmic interviews to practical, real-world assessments that predict job performance.