AI Research Engineer
Open a video or resource link, watch it, then check its box to track your progress and unlock what comes next.
- Build multimodal foundations and strong research implementation fluency
- Turn research questions into benchmarked model-improvement loops
- Scale reliable research systems and make results reproducible
Benchmark: OpenAI RSI + Multimodal Safety research roles with frontier multimodal/world-model research signals (current Sep 2026) Mastery ladder: 2 current-market competencies per level; 2 direct VIDEO bridges per competency (12 total).
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.
Build multimodal foundations and strong research implementation fluency
Native multimodal architectures and cross-modal reasoning
Coverage: Deep Learning + Computer Vision
Vision-language representation learning and model adaptation
Coverage: Multimodal + Fine-Tuning & Model Adaptation
Turn research questions into benchmarked model-improvement loops
World models, predictive representations and planning
Coverage: Deep Learning + World-Model Concepts
Evals, synthetic data and rigorous failure analysis
Coverage: AI Evals & Monitoring
Scale reliable research systems and make results reproducible
Distributed training, experiment orchestration and systems
Coverage: AI Compute, Inference & Performance Engineering
Reproducibility, experiment lineage and paper-quality evidence
Coverage: Experimentation + Git/GitHub
Role-Readiness Proof / Capstone
Run a frontier-style research program from question to prototype: formulate a hypothesis; curate/version a multimodal dataset; build strong baselines; train/adapt a VLM or multimodal model; add a predictive/world-model or agentic research experiment; design benchmarks and evals; generate targeted synthetic data; execute ablations and failure analysis; scale training; integrate the best result into a working prototype; publish a reproducible paper-style report.
Proof Rubric
- Show novelty and crisp problem formulation
- justify data/model choices
- use rigorous baselines, ablations, uncertainty and evals
- demonstrate reproducibility, lineage and compute efficiency
- diagnose failures and iterate
- build reliable research infrastructure
- communicate tradeoffs to researchers and engineers
- connect results to a working artifact and publication-quality evidence