Research Engineer, Takeoff Intel

Anthropic · Remote-Friendly (Travel Required) | San Francisco, CARemotejunior

In short

▸Ingeniero de investigación que construye pruebas y métricas para medir el avance del desarrollo de IA por IA.
▸Trabajas con datos masivos, prototipos rápidos y colaboras directamente con científicos de investigación.
▸Tu trabajo da forma a informes internos y públicos sobre el ritmo del progreso de la IA.

No se requiere inglés en el puesto, pero se requiere capacidad para leer y escribir en ing

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What they ask for

✓Haber lanzado una evaluación, producto de datos o biblioteca de investigación desde cero
✓Prototipar rápido y estar dispuesto a descartar código
✓Trabajar con datos grandes y desorganizados sin sobreingeniería
✓Haber ejecutado experimentos con modelos de lenguaje grandes
✓Trabajar desde preguntas vagas, no solo especificaciones
✓Comunicar resultados con claridad y colaborar estrechamente con investigadores

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

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Who should you write to at Anthropic?

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About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. 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 o

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