Research Engineer, Takeoff Intel

Title: Research Engineer, Takeoff Intel Location: Remote-Friendly (Travel Required) | San Francisco, CA

About the team

At Anthropic, we are delegating a growing share of AI development to AI systems themselves. Takeoff Intel is the team that measures this recursion from the inside. We're part of the Anthropic Institute. We design evaluations of AI R&D capabilities, build the internal telemetry Anthropic uses to track how much of its own model development is becoming AI-assisted, and develop the quantitative methods that turn those signals into a calibrated picture of where capability growth is heading, so that Anthropic and the wider world have accurate situational awareness on this acceleration.

Our work appears in Anthropic's model system cards (we own the AI R&D capability assessments and adapted Epoch's Capabilities Index to our evals); all the data in When AI Builds Itself comes from our team. Internally, our measurements shape research priorities and safety planning; externally, they contribute to Anthropic's public reporting on the pace of AI progress and to collaborations with third-party evaluators. We're a small team that works closely with pretraining, RL, economics, and policy researchers across the company. If you're passionate about measurement accuracy, and feel urgency about safety and situational awareness, you should consider joining us.

About the role

As a Research Engineer on Takeoff Intel you'll build and run the evaluation and measurement instruments that make this research possible. This is a generalist role on a small team: you'll work across evals infrastructure, large-scale data processing, and analysis tooling, and you'll prioritize shipping. We build instruments that answer real questions and help set priorities, not dashboards that surface noise. We value working prototypes, rapid iteration, accuracy and good prioritization. We often need to go from a vague research question to a running instrument quickly.

We're hiring at both junior and senior levels.

Responsibilities

• Design, build, and run capability evaluations and measurement instruments at scale

• Build the data and analysis pipelines that turn large volumes of model outputs and telemetry into reliable metrics

• Prototype new instruments fast, validate them, and decide what to keep

• Review and supervise AI-written code as a normal part of the workflow

• Work closely with research scientists on the team and with partner teams to define what's worth measuring

• Contribute to internal write-ups and public reporting

You may be a good fit if you

• Have shipped an evaluation, data product, or research library end to end

• Prototype fast and are comfortable throwing code away

• Handle messy, large-volume data without over-engineering

• Have run experiments on large language models, not just moved their outputs around

• Can work from a vague question rather than a spec

• Communicate results clearly and collaborate closely with the researchers whose questions your instruments answer

Strong candidates may also have

• Built evaluation harnesses or benchmark infrastructure for LLMs

• Experience with large-scale ML or data infrastructure (self-driving, observability, or similar) alongside ML exposure

• Built tools or libraries that other researchers rely on

• A track record of catching what AI-written code gets wrong

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