# Research Engineer / Research Scientist, RL Frontiers

**Company**: Anthropic
**Location**: San Francisco, CA
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Salary**: $500,000-$850,000 USD
**Category**: Engineering
**Industry**: Technology
**Wikidata**: https://www.wikidata.org/wiki/Q116758847

**Apply**: https://job-boards.greenhouse.io/anthropic/jobs/5438044008?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_e3a035c7-b8c

## Description

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.

The RL Scaling team works on how RL scales: what happens to throughput, stability, and learning efficiency as models get larger, episodes get longer, and compute grows by orders of magnitude. You will develop next-generation architectures and RL algorithms, take them from small-scale results to frontier-scale runs, and understand every place they behave differently.

**Key Responsibilities:**

- Study how RL training and sampling scale with model size, context length, and compute.

- Develop next-generation model architectures and RL algorithms.

- Take promising small-scale results to frontier-scale runs and diagnose issues.

- Build experimental infrastructure for research velocity.

- Own end-to-end performance of large RL runs.

- Build performance and cost models for proposed changes.

- Investigate training dynamics at scale.

**Minimum Qualifications:**

- Deep familiarity with modern transformer language models.

- Hands-on experience training large models in a distributed setting.

- Track record of original technical work in ML training or systems.

- Ability to design rigorous experiments at scale.

- Strong programming skills in Python and JAX or PyTorch.

**Preferred Qualifications:**

- Research experience in reinforcement learning, optimization, or large-scale training.

- Experience developing RL algorithms for language models.

- Experience with scaling laws or quantitative models of training efficiency.

- Experience designing or modifying transformer architectures.

- Experience scaling training to large fleets of accelerators.

**Representative Projects:**

- Characterize how a new RL algorithm's throughput and learning efficiency change at scale.

- Develop a new attention variant and measure its quality and throughput.

- Prepare large-scale RL runs and fix issues.

- Trace loss instabilities to root cause.

- Build models predicting throughput and cost of proposed architecture changes.

The annual compensation range for this role is $500,000-$850,000 USD.

## Skills

### Required
- transformer language models
- distributed training
- ML training or systems
- experiment design
- Python
- JAX or PyTorch

### Nice to have
- reinforcement learning
- optimization
- large-scale training
- RL algorithms for language models
- scaling laws
- transformer architectures
- accelerator scaling
- C++ or Rust

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/anthropic/jobs/5438044008?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)