Sharpwork by Chris Fildes

Designing Software Engineering Interviews That Support Neurodiverse Thinking

Chris Fildes · · 5 min read

I’m not a clinical expert in neurodiversity. I’m an engineering leader who cares about how different thinking styles appear in real teams. After neurodiversity training and seeing traditional interviews in action, I believe many hiring practices don’t match how real engineers work.

While this issue affects neurodivergent candidates most, clearer and more realistic interviews help everyone. When we base hiring on real engineering work instead of artificial tests, we have better conversations, clearer signals, and make better hires. That’s the bottom line.

Why This Matters

Engineering teams do best when they include different ways of thinking, like deep thinkers, quick pattern recognisers, structured reasoners, and creative problem solvers. But our hiring processes often filter out the very people who make teams strong.

Traditional interviews often reward people who perform well under pressure rather than those with real engineering skills. This isn’t just unfair; it’s a poor strategy.

The Problem With Traditional Interviews

Most software engineering interviews test:

  • speed instead of thoughtfulness
  • memory instead of reasoning
  • social fluency instead of clarity
  • stress performance instead of problem-solving

Whiteboards, quick questions, and showy coding exercises create an environment very different from real engineering work. For many candidates, neurodivergent or not, these formats add challenges unrelated to their ability to build software.

Why This Disadvantages Certain Neurotypes

Neurodiversity is not just one thing. It covers a range of thinking styles. People with different neurotypes experience interviews differently, and traditional formats often don't match their strengths.

ADHD

ADHD engineers are often creative, quick problem solvers, and good at spotting patterns. However, they may struggle in situations that require:

  • sustained attention under observation
  • high working-memory load
  • rapid context switching
  • long verbal explanations without visual anchors

Timed puzzles and rapid-fire questioning create friction unrelated to engineering ability.

Autism

Autistic engineers often have great clarity, precision, and deep technical focus. But they may find interviews hard when they involve:

  • ambiguous instructions
  • socially performative tasks
  • reading unstated expectations
  • being judged on communication technique rather than reasoning

They thrive in structured, predictable, well-scoped problem spaces.

Dyslexia

Dyslexic engineers are often strong in spatial reasoning, systems thinking, and seeing the big picture. However, they might struggle with:

  • reading dense text under time pressure
  • verbal processing speed
  • memory-based trivia questions

Code reading is usually fine, but rushed reading is not.

Dyspraxia

Dyspraxic engineers can be strong analytical problem solvers, but they may have trouble with:

  • tasks calling for fast manipulation of tools
  • switching rapidly between windows or contexts
  • performing under observation

They benefit from calm and structured environments.

Anxiety-Related Cognitive Load

Not technically neurodiversity, but extremely relevant.

High-pressure interviews impair:

  • working memory
  • verbal fluency
  • reasoning speed

This affects neurotypical and neurodivergent candidates alike.

What Real Engineering Actually Looks Like

Real engineering is not a timed puzzle before strangers.

Real engineering is:

  • reading existing code
  • exploring a problem
  • thinking quietly
  • asking questions
  • collaborating
  • using tools
  • reading documentation
  • designing patterns
  • debugging thoughtfully

It’s calm, deliberate, and team-oriented.

It’s asynchronous, tool-supported, and reflective.

It is not a performance. It is a process.

If interviews don’t reflect this, they’re not measuring the thing they claim to measure.

A Better Interview Model (No Live Coding, No Performance Tasks)

Here’s a model I’ve used that produces more distinct signals, calmer conversations, and far more reliable assessments of engineering ability.

A. Clear, Structured Take-Home Tasks

Small, realistic, well-scoped tasks with clear constraints.

It is not a marathon.

It is not a performance.

This reveals how someone thinks when not being watched.

B. Code Reading Instead of Code Writing

Reading code is a more accurate measure of engineering ability than writing code from scratch.

Ask things like:

  • “What do you think this function is doing?”
  • “Where would you look next?”
  • “What assumptions are you making?”

This is calm, reflective, and also predictive.

C. Architectural Reasoning

Trade-offs, patterns, clarity, simplicity - those are the real work of engineering.

Ask:

  • “How would you approach this problem?”
  • “What patterns would you consider?”
  • “What trade-offs stand out to you?”

This shows how they think, not how fast they think.

D. Problem Exploration Conversation

Not solving under pressure, but just exploring a problem together.

Let candidates:

  • ask questions
  • think quietly
  • reason through possibilities
  • explain their approach

This replicates real collaboration.

E. Questions That Reveal Working Style

These help candidates describe how they work best - and help you understand how they think, communicate, and collaborate.

Ask:

  • “What helps you think clearly?”
  • “What environment helps you bring ideas?”
  • “How do you prefer to collaborate when solving problems?”

These questions create psychological safety and show how someone works best.

What Companies Gain

Interviews that support neurodiversity are not just inclusive. They are also a smarter strategy.

Teams gain:

  • deeper thinkers
  • clearer communicators
  • stronger collaborators
  • more reliable engineers
  • better architectural reasoning
  • calmer, more thoughtful problem-solving

When interviews reflect real engineering work, companies hire people who can actually do it.

Conclusion

Human-centered engineering begins with human-centered hiring. If we want teams that think clearly and build great software, we need interviews that reflect real engineering work, not artificial performance rituals.

Thanks

I'd like to thank Owen Murray MSc from URhired for taking the time to review this article, provide valuable feedback, and offer words of encouragement.

Owen is doing important work helping neurodivergent candidates navigate the interview process and reach their potential. Through URhired's Neurodiverse Powered Interview Success platform, organisations can better support neurodivergent talent. I'm looking forward to finding out more.

References & Further Reading

Interview Design & Neurodiversity

Understanding Different Neurotypes