Research Scientist, Takeoff Intel
Anthropic · San Francisco, CA · AI Research & Engineering · listed July 24, 2026
The shape of it
Seniority
Not stated
Where
Hybrid
Stated pay
$350,000 – $850,000 USD
Requirements listed
7
Length
1,257 words
In the posting’s own words
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.
What it asks for · 7
- 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
Also a plus
- 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
What the job covers
- 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
Tools and skills named
Models & research
- Evaluations8×
- Fine-tuning3×
- LLM2×
- Machine learning2×
Go to market
- Forecasting6×
Words the posting leans on
- capability14×
- research9×
- models8×
- quantitative8×
- system8×
- evaluation7×
- experience6×
- forecasting6×
- hands-on6×
- comfortable5×
- design5×
- measure5×
- acceleration4×
- build4×
- capability growth4×
- data4×
Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.
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