SuperAnnotate

Research Engineer

San Francisco · Full-time

Sponsorship not specified$180k-$280kDetected 1 day ago

PythonDatabricksCloud PlatformsMachine LearningAgentic AIExcelResearchWriting

About the role

Our global network of expert specialists, scalable managed operations, precise talent matching, and full project transparency ensure unmatched data quality at scale.
The Impact You'll Make Our research team is expanding to keep pace with a wave of frontier-facing work: internal research streams, client engagements that require real ML depth, and emerging opportunities at the cutting edge of the field.
You won't be handed a fully specified task list; you'll be given a direction and the autonomy to turn it into a research plan.

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.
Strong Python and the engineering ability to build and ship your own experiments - eval harnesses, environments, infrastructure - without relying on a platform team.
partnering with strategic project and technical leads to scope the work, building MVPs to validate ideas (including through human annotation and agents), and turning that work into something concrete - a customer dataset, a pilot, an internal dataset that becomes a paper or blog post, or a joint publication with a partner.

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.
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.
Experience with cloud infrastructure and containerized environments.
A deep RL background specifically.
Why SuperAnnotate This is a rare opportunity to work at the intersection of frontier AI research and real production impact.
You'll work on projects with frontier labs that move the needle on model performance, with your work feeding directly into the next generation of agent capabilities.

We are an equal-opportunity employer and value diversity at our company.
At SuperAnnotate diversity means to us making an effort to reflect the many experiences and identities of the outside world, and treating each other with fairness and without bias.
Every day we foster an environment where people of all backgrounds not only belong, but excel to succeed as a company and grow together.
We offer equal opportunity regardless of sex, sexual orientation, national origin, color, race, age, marital status, disability, gender identity, veterans and more.

This listing is sourced directly from SuperAnnotate's careers page and normalized into a canonical job model.