Research Engineer: AI Scientist Infrastructure

Anthropic; California (MO); On-site; USD 350,000 - 850,000; Full time

Job summary

Anthropic, headquartered in San Francisco, seeks a Research Engineer to design and optimize infrastructure supporting AI scientist training, evaluation, and deployment across distributed environments.

You will work across the full ML stack, from data pipelines to performance tuning, collaborating with researchers to scale experimental ideas into production-ready systems and enabling safe, scalable AI research.

Qualifications

• Experience designing and operating infra for large-scale ML workloads.
• Strong background in distributed systems and production-grade infra.
• Proven ability to optimize performance in high-throughput ML environments.
• Experience with Docker/Kubernetes and cloud platforms (AWS/GCP).
• Familiarity with ML training/inference stacks (PyTorch/JAX).

Responsibilities

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

Skills

Infrastructure engineering

Distributed systems

Performance optimization

Cross-team collaboration

ML workflow knowledge

Education

Bachelor's degree

Tools

Docker

Kubernetes

Beam

Spark

Dask

PyTorch

JAX

AWS

GCP

GPU/TPU architectures

Job description

Anthropic, headquartered in San Francisco, seeks a Research Engineer to design and optimize infrastructure supporting AI scientist training, evaluation, and deployment across distributed environments.

You will work across the full ML stack, from data pipelines to performance tuning, collaborating with researchers to scale experimental ideas into production-ready systems and enabling safe, scalable AI research.