AI Research Scientist / Applied Scientist
Open a video or resource link, watch it, then check its box to track your progress and unlock what comes next.
- Form rigorous questions and build reproducible modeling foundations
- Run scale-aware experiments and build evidence that survives scrutiny
- Advance model capability through post-training, RL and research-quality iteration
Benchmark: OpenAI Research Scientist + Research Engineer/Scientist, RL/Reasoning (current Sep 2026) Market refresh: research-agenda ownership, principled experiments, scaling laws, data curation, reproducibility, evaluations, ablations, causal evidence, post-training and reinforcement learning, plus translating novel ideas into reliable large-scale systems.
Who’s Hiring Right Now
US openings posted in the last 7 days at OpenAI — this role’s own benchmark employers — refreshed daily, as of 2026-11-01. Pulled directly from each company’s public job board; not exhaustive, and postings close fast — worth confirming the details on the employer’s own site before applying.
Enrichment
Form rigorous questions and build reproducible modeling foundations
Research design, bias/variance + controlled experimentation
Coverage: Statistics / Experimental Design / Machine Learning
Deep learning + transformer implementation foundations
Coverage: Deep Learning / Generative AI / Transformers
Run scale-aware experiments and build evidence that survives scrutiny
Scaling laws, data sources + training dynamics
Coverage: Foundation Models / Data Curation / Model Training
Evaluation, ablations + causal evidence
Coverage: AI Evals / Experimentation / Causal Inference
Advance model capability through post-training, RL and research-quality iteration
Post-training, reinforcement learning + reward optimization
Coverage: Post-Training / Reinforcement Learning / Alignment
Reproducibility, failure analysis + research systems
Coverage: AI Evals / Data Curation / Research Engineering
Role-Readiness Proof / Capstone
Choose a research question with a falsifiable hypothesis. Reproduce a credible baseline; define metrics and statistical tests; build a controlled experiment with ablations; track data and code versions; analyze failures and uncertainty; then implement one meaningful model, data, post-training or inference improvement. Produce a research report with methods, results, limitations, negative findings, compute/cost and next experiments, plus a reproducible artifact.
Proof Rubric
- Show a clear hypothesis and literature-grounded rationale
- reproducible code/data
- valid baselines and controls
- sound statistics
- honest uncertainty and negative results
- meaningful ablations
- evaluation robustness
- compute and cost awareness
- evidence that the improvement generalizes
- and a concise research narrative separating observation, inference and speculation. Novelty matters only when supported by trustworthy evidence