Research Engineer, Discovery (AI)

Location: San Francisco, CA or New York City, NY; staff are expected to work from one of the offices at least 25% of the time.

Annual salary: $350,000–$850,000 USD.

Company

Anthropic develops reliable, interpretable, and steerable AI systems focused on safety and beneficial use.

What you will do

• Design and implement large-scale infrastructure for AI scientist training, evaluation, and deployment across distributed environments.
• Identify and resolve infrastructure bottlenecks affecting scientific capabilities.
• Develop evaluation frameworks for measuring progress toward scientific AGI.
• Build scalable VM, sandboxing, and container architectures for long-horizon AI tasks and scientific workflows.
• Translate experimental requirements into production-ready infrastructure and collaborate with researchers.
• Develop and optimize large-scale data, training, inference, and reinforcement learning pipelines.

Requirements

• 6+ years of highly relevant infrastructure engineering experience with demonstrated expertise in large-scale distributed systems.
• Deep knowledge of performance optimization and system architectures for high-throughput ML workloads.
• Experience with containerization technologies such as Docker and Kubernetes and orchestration at scale.
• A proven track record building large-scale data pipelines and distributed storage systems.
• Ability to diagnose and resolve complex infrastructure challenges in production environments.
• Ability to work across the full ML stack, from data pipelines through performance optimization.
• Experience collaborating with researchers to scale experimental ideas.
• Strong communication skills, collaborative working style, and ability to iterate rapidly from experimentation to production.
• Bachelor’s degree or equivalent combination of education, training, and relevant experience in a field demonstrated through coursework, training, or professional experience.

Nice to have

• Experience with language model training infrastructure and distributed ML frameworks such as PyTorch or JAX.
• Infrastructure experience in AI research labs or large-scale ML organizations.
• Knowledge of GPU/TPU architectures and language model inference optimization.
• Enterprise-scale experience with AWS or GCP.
• Familiarity with VM and container orchestration, workflow orchestration tools, and experiment management systems.
• Experience with large-scale reinforcement learning and data pipelines using tools such as Beam, Spark, or Dask.

Culture & Benefits

• Collaborative research environment organized around large-scale efforts to advance trustworthy AI.
• Flexible working hours and office-based collaboration.
• Generous vacation and parental leave.
• Competitive compensation and optional equity donation matching.
• Visa sponsorship may be available, with immigration lawyer support, depending on the role and candidate.