Research Engineer
HybridSan Francisco, California, United States
San Francisco, California, United StatesHybridFull TimeSmall
Full TimeSmall
Job Summary
Implement research directions by independently identifying papers, benchmarks, and prior work, then reimplementing relevant methods and building processes to reproduce and improve upon them. Own projects end to end, including scoping, MVP implementation, and validation, while partnering with technical leads to translate ambiguous requirements into concrete, testable research plans. Validate ideas through hands-on implementation, including annotating, evaluating, or sourcing data, and turn research into tangible outputs such as customer datasets, pilots, or publications for conference presentation.
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 of how models are trained and evaluated
- ability to read a paper
- ability to judge whether its claims hold
- ability to reimplement the 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
- ability to notice when something's off before being told
- 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
- building RL environments/gyms
- Familiarity with reward modeling
- reward hacking
- verifier/judge reliability
- Familiarity with synthetic data generation
- human-in-the-loop (HITL) workflows
- Experience with cloud infrastructure
- containerized environments
- A deep RL background specifically
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