Engineering Ic interview prep.

Behavioural questions to expect

1. Walk me through your CV.
2. Tell me about your most impactful ML systems project.
3. Tell me about a weakness, a failure, or feedback you've received and worked on.
4. Why ML engineering - and why this firm vs generic SWE or research?
5. Which team or area would you want to work on, and why?
6. Why the firm?
7. How would you describe the firm's ML engineering organisation in your own words?
8. How does ML engineering actually create value at an AI platform firm?

Technical concepts to master

• Distributed training + parallelism

  Parallelism strategy selection · Communication + topology · Training stability + recovery · MFU + HFU

• Inference serving + low-latency

  Continuous batching · KV cache · Quantization + speculative decoding · Multi-replica + autoscaling

• Data pipelines + feature stores + training-serving consistency

  Data pipeline at training scale · Feature store · Data drift + quality · Labeling + active learning

• MLOps + deployment + monitoring + drift

  Deployment patterns - canary, shadow, A/B · Model monitoring · Drift detection + retraining · Rollback + safe degradation

Practical drills

• Design distributed training for a 100B-parameter transformer LLM on 1024 GPUs. Walk me through.
• Design low-latency LLM inference serving for the firm's API at 100K QPS with P99 < 100ms TTFT + <50ms per-token.
• Training is at 30% MFU on 64 H100s. Walk me through how you'd diagnose + improve.

Smart-question anchors

• Team + scope - team's surface area, what the role would own in 6-12 months
• Stack + scale - training cluster size, framework, inference scale, hardware investment
• Research-engineering collaboration - how research becomes product, RFC + design review culture
• On-call + reliability - training-run reliability, inference SLO, postmortem culture
• Cost + efficiency - GPU utilization targets, FinOps maturity, recent efficiency programs

Sourced from

interviewing.io + Hello Interview + IGotAnOffer — system design canon · ML systems literature (distributed training + parallelism) · MLOps + production ML literature (Google ML system design / Continuous Delivery for ML) · Inference serving + GPU optimization literature (NVIDIA tech blogs + practitioner content) · Tech Interview Handbook + Eng Leadership Newsletter — behavioral · Frontier-lab ML engineering blogs (OpenAI / Anthropic / DeepMind / Meta AI engineering content)