# Research Engineer / Performance Engineer, RL Distributed Systems

**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/5438030008?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_72049630-462

## Description

## Job Overview

As a Research Engineer on the Distributed Systems team within RL Engineering at Anthropic, you will work on designing, building, and operating the distributed systems that run RL at scale. This involves finding and removing limitations in the system, building fault tolerance, and designing resource management and autoscaling.

## Key Responsibilities

- Design, build, and operate distributed systems for RL at scale

- Identify and remove system limitations

- Implement fault tolerance and recovery mechanisms

- Design resource management and autoscaling policies

- Develop observability and automation tools

- Collaborate with researchers and performance engineers

## Requirements

- Strong software engineering skills in Python and a systems language

- Experience with large-scale distributed systems

- Understanding of distributed systems fundamentals

- Quantitative reasoning about throughput, latency, and resource costs

- Experience debugging complex failures

- Strong written communication skills

## Preferred Qualifications

- Experience with ML training or inference infrastructure

- Knowledge of container orchestration and sandboxed code execution

- Familiarity with high-performance networking and collective communication libraries

- Experience with observability and automated remediation

- Async Python frameworks experience

- Reinforcement learning or large language model training experience

## Logistics

- Annual salary: $500,000 - $850,000 USD

- Bachelor's degree or equivalent

- Field relevant to the role

- Hybrid policy requiring at least 25% office time

## Skills

### Required
- Python
- distributed systems
- fault tolerance
- resource management
- autoscaling
- observability
- debugging
- written communication

### Nice to have
- ML training
- inference infrastructure
- container orchestration
- sandboxed code execution
- high-performance networking
- collective communication libraries
- async Python frameworks
- reinforcement learning
- large language models

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/anthropic/jobs/5438030008?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)