Prepare for Research Engineer interviews at frontier AI labs.
Discover what you can demonstrate independently, where your reasoning breaks down, and what to practise next.
Find what to practise next
A timed adaptive fundamentals check. Calculations lead into debugging and decision follow-ups, and the result recommends your next session. It's a review tool, not a readiness verdict.
Gap
Identify a missed padding-mask bug.
Practice
Debug a different attention implementation.
Evidence
Fix it independently, add a regression test, and explain why.
What every Research Engineer should be able to demonstrate
The emphasis changes by team and role; these capabilities remain shared.
Algorithms, data structures, numerical code, and PyTorch.
Threads, async execution, synchronization, scheduling, failure recovery, and distributed coordination.
Objectives, gradients, optimization, probability, and evaluation from first principles.
Controlled hypotheses, confounders, leakage, ablations, and misleading gains.
Training and inference performance, GPU memory, batching, and distributed ML workloads.
Optimize an LLM inference stack →Explain work, separate evidence from speculation, and propose the next experiment.
Round types: estimate, diagnose, design, code
- 01
Estimate
Size memory, bandwidth and cost from first principles before deciding.
- 02
Diagnose
Read the evidence and name the failure that survives it.
- 03
Design
Choose between plans and defend the trade-off.
- 04
Code
Implement it against seeded tests, edge cases and memory checks.
Every Practice problem is a scenario worked in 3–5 steps, and each step is tagged with one of these rounds.
Go deeper for your target role
Choose based on your target role while the assessment identifies any foundation gaps to practise alongside it. You can switch or complete both.