Applied AI Research Scientist - Frontier RL & Data Systems

Meta; Burlingame (CA); On-site; USD 154,000 - 217,000; Full time

Job summary

Meta is seeking a Research Scientist for the Agent Data and Optimization team within the Applied AI org. You will independently own ambitious applied research from question to measured result, developing RL environments, data pipelines and evaluation benchmarks that advance the Muse family of models and the Muse assistant.

You’ll identify capability gaps, formulate hypotheses, build experiments and collaborate across model development and research teams to turn ideas into reproducible methods

Qualifications

• PhD in Computer Science, AI, or a related technical field, or equivalent practical experience.
• 3+ years leading major research initiatives or influencing technical strategy across teams.
• Proven ability to build reliable technical systems and evaluate their results.
• Strong track record of first-author publications on AI topics with conference or journal presentations.
• Bachelor's degree in Computer Science, Computer Engineering, or related field.

Responsibilities

• Identify capability gaps and formulate research questions that advance AI agents’ reasoning and problem-solving abilities.
• 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 to new problems.
• Lead research from hypothesis through implementation and measured results, collaborating with Meta’s model development and research teams.

Skills

PhD experience in AI

Research leadership

Experiment design

Collaboration

Education

PhD in Computer Science/AI

Bachelor's in CS/CE

Job description

Meta is seeking a Research Scientist for the Agent Data and Optimization team within the Applied AI org. You will independently own ambitious applied research from question to measured result, developing RL environments, data pipelines and evaluation benchmarks that advance the Muse family of models and the Muse assistant.

You’ll identify capability gaps, formulate hypotheses, build experiments and collaborate across model development and research teams to turn ideas into reproducible methods