Anthropic’s mission is to create reliable, interpretable, and steerable AI systems that are safe and beneficial for users and society. The Research Scientist role focuses on hands‑on research on large models—including pretraining, fine‑tuning, reinforcement learning, evaluations, and agent systems—to measure and understand recursive‑self‑improvement. The position emphasizes quantitative modeling, experimental design, and producing graded assessments for internal decision makers, while collaborating across pretraining, RL, economic research, and policy teams.
Research Scientist, Takeoff Intel at Anthropic
Hybrid - San Francisco, CA, USA | New York City, NY, USA
More jobs at AnthropicSalary
USD 350,000 - 850,000
Requirements
Skills
- Hands‑on research on large language models: pretraining, fine‑tuning, RL, evals, or agent systems
- Strong quantitative instincts, comfortable with quantitative modeling and reasoning
- Experience in forecasting, may have published AI forecasting scenarios
- Can design an evaluation from a vague question and defend the methodology
- Write clearly and calibrate: state confidence, name what would change your conclusion
- Motivated by impact: comfortable with work whose output is graded assessments and system‑card sections more often than papers
- Care about AI safety and think carefully about where rapid capability growth leads
- Trained or RL'd frontier models hands‑on
- Experience with scaling laws, capability forecasting, or emergent‑capability studies
- Physics, applied‑math, or similarly quantitative background that moved into ML
- Written a system card section, capability report, or methodology document that others cite
- Experience supervising and correcting AI‑written code
- Bachelor’s degree or an equivalent combination of education, training, and/or experience
- Field relevant to the role as demonstrated through coursework, training, or professional experience
Responsibilities
- Identify the signals that track AI R&D acceleration and design the evaluations that measure them
- Build quantitative models of capability growth and self‑improvement dynamics, grounded in evaluation and telemetry data
- Run experiments and evals to test hypotheses about automation and capability
- Make opinionated research bets and own the outcome
- Write graded assessments of what our measurements show, for internal decision‑makers and public reporting
- Collaborate with pretraining, RL, economic research, and policy teams
See if your resume is ready for this job
See how our AI can optimize your resume and improve your chances for this role.