# Research Engineer / Research Scientist, RL Frontiers at Anthropic

> Open role observed by RunReq as of 2026-10-15T00:27:52.219163+00:00.

## Role details

- Company: [Anthropic](http://runreq.com/company/anthropic/index.md)
- Location: San Francisco, CA \| New York City, NY \| Seattle, WA
- Team: AI Research & Engineering
- Compensation: $500K–$850K /YR
- First observed: 2026-09-30T01:48:06.179205+00:00
- Days open: 15
- Employer posting: [View the authoritative posting](https://job-boards.greenhouse.io/anthropic/jobs/5438044008)

## Shortened description

### Key responsibilities

- Study how reinforcement learning training and sampling change with model size, context length, and compute, then improve efficiency.
- Develop architectures and reinforcement learning algorithms, run them at frontier scale, and diagnose numerical, algorithmic, or system problems.
- Build experimental infrastructure for fast, reproducible comparisons of architecture and algorithm variants at meaningful scale.
- Own large reinforcement learning runs from research code through hardware, and investigate instability, divergence, and throughput regressions.

### Qualifications needed

- You hold a bachelor’s degree or equivalent education, training, or experience in a relevant field.
- You understand transformer language models, including architecture, training dynamics, and large-scale optimization.
- You have trained large models in distributed systems using data, tensor, and pipeline parallelism.
- You show original technical work, rigorous experiments, quantitative cost analysis, and strong Python and JAX or PyTorch programming.

## Hiring context

- Open roles on this team: 66
- Newly observed on this team in 30 days: 7
- Company median days open: 55

## Interpretation

- RunReq records observed job postings; it does not show applicant volume or completed hires.
- The linked employer posting is authoritative for the full description and application process.
- Human-readable job page: http://runreq.com/jobs/39fc41ad-4d39-8853-9d25-a9f00bf5d0e6
- Site-wide agent directory: http://runreq.com/llms.txt