Research Engineer

SuperAnnotate AI • San Francisco, CA

112d • $180,000 - $250,000 • Hybrid

About The Position

SuperAnnotate is seeking a Research Engineer to contribute to frontier-facing AI research across internal research streams, client engagements, and emerging opportunities. The role involves independently researching relevant papers, benchmarks, and prior work, reimplementing applicable methods, and building internal processes to reproduce and improve on existing research. The Research Engineer will own projects from scoping through implementation and validation, collaborating with strategic project and technical leads to turn ambiguous directions into testable research plans. Outputs may include customer datasets, pilots, internal datasets, papers, blog posts, or joint publications. The position is full-time and hybrid, based in San Francisco.

Requirements

• MS or PhD in ML, CS, or a related quantitative field, or equivalent demonstrated research experience through publications, significant open-source research work, or industry research.
• Strong machine learning knowledge, including understanding how models are trained and evaluated, evaluating research claims, and reimplementing methods.
• Hands-on experience in at least one of RL or agentic systems, AI/ML evaluation and benchmarking, or multimodal ML.
• Strong Python skills and the engineering ability to build and ship experiments, such as evaluation harnesses, environments, and infrastructure, without relying on a platform team.
• High autonomy, including the ability to turn an ambiguous direction into a concrete research plan and identify issues independently.
• Clear technical writing.

Nice To Haves

• Publication track record, preferably as a first author.
• Experience with agent or multimodal benchmarks such as OSWorld, MMMU, WebArena, or SWE-bench, or experience building RL environments or gyms.
• Familiarity with reward modeling, reward hacking, or verifier/judge reliability.
• Familiarity with synthetic data generation or human-in-the-loop workflows.
• Experience with cloud infrastructure and containerized environments.
• A deep reinforcement learning background.

Responsibilities

• Independently identify relevant research resources, including papers, benchmarks, and blog posts, and implement or reimplement applicable methods.
• Build and own processes for reproducing prior work internally and identify opportunities to improve it.
• Own projects end to end, including scoping, MVP implementation, and validation.
• Partner with strategic project leads and technical leads to translate ambiguous requirements into concrete, testable research plans.
• Validate ideas through hands-on implementation, including annotating, evaluating, or sourcing data.
• Turn research directions into tangible outputs such as customer datasets, customer pilots, internal datasets, papers, or blog posts for publication or conference presentation.
• Assess the technical feasibility of incoming requests and help shape proposals that require research depth.

Benefits

• Annual bonus paid out quarterly in addition to annual base salary.
• Opportunity to work on projects with frontier labs, publish research, and present at conferences.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level