Hiring and interviewing AI talent demands a clear understanding of each position’s unique motivations, skill sets, and interview focus areas.
This guide breaks down three of the most sought-after roles in AI—AI Platform Engineer, AI Research Scientist, and Machine Learning Engineer. It outlines how to effectively interview candidates for each.
We explore what drives professionals in these roles, the expertise they bring, and how to assess them during interviews.
You’ll find actionable insights, sample questions, and evaluation criteria drawn from industry best practices at Google, Meta, and Microsoft.
AI Platform Engineer
These engineers build and optimize the infrastructure that powers AI systems. They focus on scalability, reliability, and performance.
Motivations: Solving complex systems problems, enabling high-performance AI workloads, and building robust platforms.
Key Skills: Distributed systems, cloud architecture, containerization (e.g., Kubernetes), CI/CD pipelines, and performance tuning.
Interview Focus
Systems design: Ask candidates to architect scalable AI infrastructure.
Debugging and optimization: Present real-world latency or throughput issues.
Collaboration: Explore how they work with data scientists and ML engineers.
AI Research Scientist
These professionals push the boundaries of AI theory and develop novel algorithms.
Motivations: Advancing the state of the art, publishing research, and solving unsolved problems.
Key Skills: Deep learning, reinforcement learning, generative models, and mathematical modeling.
Interview Focus
Research depth: Ask about recent papers they’ve read or authored.
Problem formulation: Present open-ended challenges and assess their approach.
Innovation: Evaluate their ability to propose novel solutions or improvements.
Machine Learning Engineer
ML Engineers implement and scale models for production use.
Motivations: Applying models to real-world problems, improving performance, and deploying reliable systems.
Key Skills: Model development, feature engineering, data pipelines, and MLOps.
Interview Focus
Model implementation: Ask them to build or improve a model based on a dataset.
Deployment: Explore their experience with model serving and monitoring.
Experimentation: Assess how they measure and iterate on model performance.
Interviewing AI Talent: A Strategic Approach
Hiring top-tier AI professionals demands a strategic and thorough approach.
To engage candidates effectively, tailor your conversations to what excites them:
AI Platform Engineers thrive on discussions about scalability, infrastructure, and tooling.
AI Research Scientists light up when exploring novel ideas and theoretical breakthroughs.
Machine Learning Engineers focus on building practical, impactful AI solutions.
Use these insights to build rapport and ask meaningful, role-specific questions.
Assess Skills with Precision
Structure your interviews to evaluate both technical depth and collaborative ability. Draw from proven industry practices:
Blend technical assessments: Include coding challenges, theoretical questions, and system design exercises tailored to each role.
Explore real-world experience: Use behavioural questions to uncover how candidates navigate challenges, collaborate, and drive results.
Dive deep with researchers: Allocate time for technical discussions or presentations of their work to assess originality and rigor.
Final Thoughts for Interviewing AI Talent
Throughout the interview process, align your expectations with the core competencies of each role. Be prepared to answer candidates’ questions—top-tier talent will evaluate your company just as thoroughly as you evaluate them.
AI Research Scientists may ask about publication support, access to compute resources, or opportunities for long-term exploration.
AI Platform Engineers often inquire about your tech stack, infrastructure, and DevOps culture.
Machine Learning Engineers typically want to understand the roadmap for ML products and how their work will impact users.
Providing thoughtful, transparent answers shows that your organization values their expertise and fosters a culture of innovation.
Hire with Confidence
By following this guide—built on insights from Google, Meta, Microsoft, and broader industry benchmarks—you’ll be equipped to interview AI talent and hire the best.
Focus on both technical excellence and team fit. Probe for depth in their specialty and breadth in their ability to collaborate and drive projects forward.