Research Scientist, Takeoff Intel

About the role

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

You may be a good fit if you

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

Strong candidates may also 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

Some examples of our work

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

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:

$350,000—$850,000 USD