Research Engineer

SuperAnnotate AI•San Francisco, CA

1d•$180,000 - $280,000•Hybrid

About The Position

SuperAnnotate is seeking a Research Engineer to join their expanding research team. This role involves working on frontier-facing AI research, including internal research streams, client engagements, and emerging opportunities. The Research Engineer will be responsible for taking a research direction, identifying relevant papers and benchmarks, reimplementing methods, and developing processes for reproduction and improvement. The role requires end-to-end ownership of initiatives, partnering with project and technical leads to scope work, build MVPs, and translate research into tangible outputs such as customer datasets, pilots, internal datasets, or publications. The position offers autonomy in developing research plans and is a full-time, hybrid role based in San Francisco.

Requirements

MS or PhD in ML, CS, or a related quantitative field – or equivalent demonstrated research experience (publications, significant open-source research work, industry research).
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.
Hands-on experience with at least one of: RL/agentic systems, AI/ML evaluation and benchmarking, or multimodal ML.
Strong Python and the engineering ability to build and ship your own experiments – eval harnesses, environments, infrastructure – without relying on a platform team.
High autonomy: you can turn an ambiguous direction into a concrete research plan and notice when something's off before being told.
Clear technical writing

Nice To Haves

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.
A deep RL background specifically.

Responsibilities

Take a research direction and independently identify supporting resources – papers, benchmarks, blog posts – then implement or reimplement the relevant methods.
Build and own the process to reproduce prior work internally and identify ways to improve on it.
Own projects (for example, an RL/agentic environment build for a partner or a novel multimodal benchmark) end to end, including scoping, MVP implementation, and validation.
Partner with strategic project leads and technical leads to translate ambiguous requirements into a concrete, testable research plan.
Validate ideas through hands-on implementation, including annotating, evaluating, or sourcing data.
Turn research directions into tangible outputs – a paid customer dataset, a customer pilot, an internal dataset, or a paper/blog post for publication or conference presentation.
Bring an ML perspective to new opportunities — assessing technical feasibility of incoming requests and helping shape proposals where research depth is needed.