Hybrid ML Research Engineer — Frontier AI

SuperAnnotate AI; San Francisco (CA); Hybrid; USD 180,000 - 280,000; Full time

Benefits offered by this job

Hybrid work model

Annual bonus

Job summary

SuperAnnotate AI in San Francisco is seeking a Research Engineer to lead frontier AI research initiatives. You will identify relevant papers and benchmarks, reimplement methods, and build end-to-end projects with autonomy and rigor.

You’ll own research plans, reproduce prior work, and turn directions into tangible outputs such as datasets, pilots, or papers. The role combines research depth with practical software engineering and clear technical writing.

Qualifications

• MS or PhD in ML, CS, or related quantitative field or equivalent demonstrated research experience.
• Real ML depth: understanding model training and evaluation, ability to read and reimplement papers.
• Hands-on experience with RL/agentic systems, AI/ML evaluation/benchmarking, or multimodal ML.
• Strong Python and engineering ability to build and ship experiments (eval harnesses, environments, infrastructure).
• High autonomy to turn ambiguous directions into concrete research plans.
• Clear technical writing.

Responsibilities

• Identify supporting resources (papers, benchmarks) and implement relevant methods.
• Build and own processes to reproduce prior work and improve outcomes.
• Own end-to-end projects (RL/agentic envs, multimodal benchmarks) from scoping to validation.
• Translate requirements into a concrete, testable research plan.
• Validate ideas through hands-on implementation and data evaluation.
• Turn research directions into tangible outputs (datasets, pilots, papers).
• Contribute technical depth to new opportunities and proposals.

Skills

ML depth

Python

Autonomy

Technical writing

Education

MS/PhD in ML/CS

Tools

Eval harnesses

Environments

Infrastructure

Job description

SuperAnnotate AI in San Francisco is seeking a Research Engineer to lead frontier AI research initiatives. You will identify relevant papers and benchmarks, reimplement methods, and build end-to-end projects with autonomy and rigor.

You’ll own research plans, reproduce prior work, and turn directions into tangible outputs such as datasets, pilots, or papers. The role combines research depth with practical software engineering and clear technical writing.