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Research Scientist, Takeoff Intel
Description
We're looking for a Research Scientist who has done hands-on research on large models (pretraining, fine-tuning, RL, evals, or agents scaffolds) and wants to focus on measuring and understanding recursive-self-improvement. You know what the model-development loop looks like from the inside: which signals matter and where the real bottlenecks are. On this team you'll use that judgment to decide what's worth measuring, design the evaluations and models that measure it, and interpret what the results mean for how fast this is moving.
We're hiring at both junior and senior levels. Senior researchers should be comfortable doing hands-on technical work alongside setting research direction.
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
Qualifications
Have done hands-on research on large language models: pretraining, fine-tuning, RL, evals, or agent systems
Have strong quantitative instincts, are comfortable with quantitative modeling and reasoning
Have 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
Are 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
Nice to have
Trained or RL'd frontier models hands-on
Experience with scaling laws, capability forecasting, or emergent-capability studies
A 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
Anthropic ECI: our adaptation of Epoch Capabilities Index published in all recent system cards to measure capability acceleration
AI R&D capability assessments in the Claude system cards
When AI Builds Itself: all data in the article comes from our team
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