# Research Engineer, RL Scaling Science

**Company**: Anthropic
**Location**: London
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Salary**: £375,000-£640,000 GBP
**Category**: Engineering
**Industry**: Technology
**Wikidata**: https://www.wikidata.org/wiki/Q116758847

**Apply**: https://job-boards.greenhouse.io/anthropic/jobs/5264619008?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_3b093857-176

## Description

Anthropic's mission is to create reliable, interpretable, and steerable AI systems.

As a Research Engineer on the RL Scaling Science team, you'll design and run large-scale experiments to understand and resolve bottlenecks, build benchmarks for long-horizon progress, and ship validated findings into production training.

## Key Responsibilities

- Design, run, and interpret large-scale RL experiments

- Investigate how RL improves with horizon, compute, and model size growth

- Build and maintain benchmarks for long-horizon RL

- Translate validated findings into production training recipes

- Debug complex issues at the research-infrastructure boundary

- Partner with adjacent RL teams to advance the RL stack

## Minimum Qualifications

- Strong empirical research skills in Reinforcement Learning or related areas

- Ability to own large experiments end-to-end

- Proficiency in Python and experience with large-scale ML systems

- Comfort operating at the research-systems boundary

- Care about AI's societal impacts and responsible scaling

## Preferred Qualifications

- Published or shipped work in long-horizon RL or RL fundamentals

- Experience translating research findings into production training recipes

- Demonstrated large-scale industry impact via RL interventions

- Experience with frontier-scale training runs

## Logistics

- Annual Salary: £375,000-£640,000 GBP

- Location-based hybrid policy: 25% office time

- Visa sponsorship available

## Skills

### Required
- Reinforcement Learning
- Python
- large-scale ML systems
- empirical research

### Nice to have
- long-horizon RL
- production training recipes
- frontier-scale training runs

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/anthropic/jobs/5264619008?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)