Frontier AI Research Scientist (RL & Environments)

Meta; Seattle (WA); On-site; USD 154,000 - 217,000; Full time

Benefits offered by this job

Bonus

Equity / Stock options

Benefits

Job summary

Meta's Agent Data and Optimization team is seeking a Research Scientist to own applied research from hypothesis to measured results, building RL environments and evaluation benchmarks for Muse models. You will identify capability gaps, design experiments, curate data pipelines, and analyze agent behavior to distinguish genuine progress from shortcuts, working closely with model developers.

This role blends research judgment with hands-on engineering in a startup-like, well-funded setting to push

Qualifications

• PhD in Computer Science, AI, or equivalent practical experience.
• 3+ years of experience leading major research initiatives or influencing technical strategy.
• Proven ability to build reliable technical systems and evaluate their results.
• Strong track record of first-author publications and presenting AI work.
• Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.

Responsibilities

• Identify capability gaps and formulate research questions that advance AI agents’ reasoning and problem solving.
• Develop research methods, benchmarks and experimental environments with reliable, reproducible measurements.
• Create and curate high-quality data, and build pipelines that support model training and evaluation.
• Analyze agent behavior and design feedback that rewards useful progress and tests generalization.
• Lead research from hypothesis through implementation and measured results, collaborating with Meta’s model development and research teams.

Skills

Lead research initiatives

Influence technical strategy

Build reliable systems

Publish AI research

Present research results

Education

PhD in Computer Science or AI

Bachelor's in CS/CS Eng

Job description

Meta's Agent Data and Optimization team is seeking a Research Scientist to own applied research from hypothesis to measured results, building RL environments and evaluation benchmarks for Muse models. You will identify capability gaps, design experiments, curate data pipelines, and analyze agent behavior to distinguish genuine progress from shortcuts, working closely with model developers.

This role blends research judgment with hands-on engineering in a startup-like, well-funded setting to push