Research Engineer
HybridSan Francisco, California, United States
Job Summary
Implement research directions by identifying papers, benchmarks, and prior work, then reimplementing relevant methods to build internal processes for reproduction and improvement. Own projects end to end, including scoping, MVP implementation for validation, and translating ambiguous requirements into concrete, testable research plans. Partner with strategic and technical leads to validate ideas through hands-on implementation, data annotation, evaluation, or sourcing. Deliver tangible outputs such as customer datasets, pilots, internal datasets, or publications for conferences. Bring an ML perspective to new opportunities by assessing technical feasibility and shaping proposals requiring research depth.
Required Qualifications
- MS or PhD in ML, CS, or a related quantitative field
- equivalent demonstrated research experience (publications, significant open-source research work, industry research)
- Real ML depth: understanding how models are trained and evaluated
- ability to read a paper and judge whether its claims hold
- ability to reimplement a method
- Hands-on experience with at least one of: RL/agentic systems, AI/ML evaluation and benchmarking, or multimodal ML
- Strong Python
- engineering ability to build and ship your own experiments - eval harnesses, environments, infrastructure
- High autonomy: ability to turn an ambiguous direction into a concrete research plan
- Clear technical writing
- This is a full-time, hybrid position based in San Francisco
Desired Qualifications
- Publication track record (first-author preferred)
- Experience with agent or multimodal benchmarks (OSWorld, MMMU, WebArena, SWE-bench, or similar) or building RL environments/gyms
- Familiarity with reward modeling, reward hacking, or verifier/judge reliability
- Familiarity with synthetic data generation or human-in-the-loop (HITL) workflows
- Experience with cloud infrastructure and containerized environments
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