Role tracks
Choose your next challenge
Select your role, choose a format and start practising.
Research Engineer
Research experimentation, model training and evaluation
TrainingEvalsAlignment
Typical loop
- Recruiter screen30m
- Hiring manager / technical screen60m
- Take-home (written)90m
- Live coding / pair programming60m
- Research / experimental design discussion60m
- Systems / ML infra design60m
- Cross-functional collaboration45m
- Founder / exec final45m
- Deep diveConstitutional AI failure-modes research memoConstitutional AI45 min
- Deep diveDeliberative alignment failure-modes research memoDeliberative Alignment45 min
- Deep diveEval design memo: deployment eligibility for autonomous task executionEval Design60 min
- Deep diveResearch memo: diagnosing a mid-run eval declineTraining Diagnostics60 min
- Deep diveResearch memo: failure modes in our next RLHF training runAlignment Research60 min
- Deep diveResearch memo: paraphrase-recall asymmetry investigationReversal Curse58 min
- Deep diveResearch memo: refusal-bypass mechanism investigationRefusal58 min
- Deep diveResearch memo: sycophancy correction-signal-resistance investigationSycophancy60 min
- Deep diveRLHF failure-modes research memoRLHF45 min
- Deep diveScalable oversight failure-modes research memoScalable Oversight45 min
- CodingClassifier with Noisy AnnotatorsData Quality60 min
- CodingData Labeling Task SchedulerConstructive Scheduling60 min
- CodingDNS ResolverProgressive Systems Coding90 min
- CodingMusic NotationProgressive String Processing60 min
- CodingSchedule labeling work day by dayIncremental Scheduling60 min
- CodingVersion Dependency (Binary Search)Progressive Search75 min
- CodingWeighted data batcher, one part at a timeData Pipelines55 min
- Rapid fireAutograd & Backpropagation Fundamentals 1Autograd & backpropagation · 7 questions8 min
- Rapid fireDistributed Training Fundamentals 1Distributed training · 7 questions8 min
- Rapid fireLoss & Entropy Fundamentals 1Loss and entropy · 7 questions8 min
- Rapid fireNumerical Precision Fundamentals 1Numerical precision · 7 questions8 min
- Rapid firePost-Training Fundamentals 1Post-training · 7 questions8 min
- Mini caseAnnotator-quality triageHuman data quality12 min
- Mini caseCheckpoint restore bug huntState persistence14 min
- Mini caseDistributed training instabilityTraining Systems12 min
- Mini caseExperiment review: post-training resultExperimental rigor14 min
- Mini caseHuman and AI task router bug huntTask allocation14 min
- Mini caseLLM output processing bug huntModel inference14 min
- Mini caseLow GPU throughput triageTraining performance12 min
- Mini caseReview a large training-run budgetTraining systems15 min
- Mini caseSpot the bugs: ring all-gatherDistributed Training13 min
- Mini caseSpot the bugs: SFT loss stepFine-tuning12 min
- Mini caseSpot the bugs: transformer inferenceModel Internals15 min
- Mini caseTTL database bug huntData systems14 min