Research Engineer: AI Scientist Infra & Pipelines

Anthropic; San Francisco (CA); Hybrid; USD 350,000 - 850,000; Full time

Benefits offered by this job

Flexible hours

Generous vacation

Parental leave

SF office

Equity donation matching

Job summary

Anthropic, a leading AI safety and research company, is seeking a Research Engineer to own end-to-end infrastructure across the model stack. You will tackle performance optimization, distributed systems, VM/sandboxing, and data pipelines to enable scalable AI scientist training, evaluation, and deployment.

Ideal candidates bring 6+ years in infra engineering, strong collaboration, and deep ML workload optimization expertise, with experience in Docker/Kubernetes and modern ML frameworks.

Qualifications

• 6+ years of infrastructure engineering experience with large-scale distributed systems.
• Strong communication and collaboration skills.
• Deep knowledge of performance optimization for high-throughput ML workloads.

Responsibilities

• Design and implement large-scale infra systems to support AI scientist training, evaluation, and deployment across distributed environments.
• Identify and resolve infra bottlenecks hindering scientific capabilities.
• Develop robust evaluation frameworks to measure progress toward scientific AGI.
• Build scalable VM/sandboxing/container architectures for long-horizon AI tasks and workflows.
• Collaborate to translate experimental requirements into production-ready infrastructure.
• Develop large-scale data pipelines for language model training requirements.
• Optimize training and inference pipelines for stable, efficient RL.

Skills

Distributed systems

Performance optimization

Communication

Research collaboration

ML workflows

Education

Bachelor's degree

Tools

Docker

Kubernetes

PyTorch

JAX

Job description

Anthropic, a leading AI safety and research company, is seeking a Research Engineer to own end-to-end infrastructure across the model stack. You will tackle performance optimization, distributed systems, VM/sandboxing, and data pipelines to enable scalable AI scientist training, evaluation, and deployment.

Ideal candidates bring 6+ years in infra engineering, strong collaboration, and deep ML workload optimization expertise, with experience in Docker/Kubernetes and modern ML frameworks.