Research Engineer

Join our expanding research team as a Research Engineer. You will take a research direction and independently identify supporting resources, implement relevant methods, and build a process to reproduce and improve prior work. You will own projects end to end, partner with strategic project leads and technical leads, validate ideas through hands-on implementation, and turn research directions into tangible outputs. You should have a strong background in ML, CS, or a related quantitative field, experience with Python, and a publication track record.

• Identifying and implementing relevant research methods, benchmarks, and prior work to support research directions.
• Owning projects end to end, including scoping, MVP implementation, and validation, and turning research directions into tangible outputs.
• Partnering with strategic project leads and technical leads to translate ambiguous requirements into a concrete, testable research plan.

• Clear technical writing
• MS or PhD in ML, CS, or a related quantitative field – or equivalent demonstrated research experience (publications, significant open-source research work, industry research)
• Strong Python and the engineering ability to build and ship your own experiments – eval harnesses, environments, infrastructure – without relying on a platform team
• Hands-on experience with at least one of: RL/agentic systems, AI/ML evaluation and benchmarking, or multimodal ML
• Real ML depth: you understand how models are trained and evaluated, not just how to call an API. You can read a paper, judge whether its claims hold, and reimplement the method
• High autonomy: you can turn an ambiguous direction into a concrete research plan and notice when something's off before being told
• 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
• A deep RL background specifically
• Experience with cloud infrastructure and containerized environments