[C] Research Engineer, Discovery

Anthropic•San Francisco, CA

12h•$350,000 - $850,000•Hybrid

About The Position

Anthropic is seeking a Research Engineer for a team focused on building an AI scientist: a system capable of solving long-term reasoning challenges and developing the capabilities needed to push the scientific frontier. The role works end to end across the model stack to identify and address infrastructure blockers to scientific AGI. Candidates should be familiar with aspects of language model training, evaluation, and inference, and be eager to learn areas beyond their current expertise. Work may involve performance optimization, distributed systems, VM, sandboxing and container deployment, and large-scale data pipelines. The position involves designing and implementing infrastructure for AI scientist training, evaluation, and deployment across distributed environments; resolving bottlenecks; developing evaluation frameworks; building safe, scalable execution environments for long-horizon tasks and scientific workflows; and optimizing training, inference, and reinforcement-learning pipelines. The role also collaborates with researchers to turn experimental needs into production-ready infrastructure. Anthropic describes itself as a public benefit corporation headquartered in San Francisco, working to create reliable, interpretable, and steerable AI systems that are safe and beneficial. Staff are currently expected to work from an office at least 25% of the time, though some roles may require more. The position offers competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office space for collaboration.

Requirements

6+ years of highly relevant infrastructure engineering experience, with demonstrated expertise in large-scale distributed systems.
Strong communication skills and an ability to work collaboratively.
Deep knowledge of performance optimization techniques 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 to performance optimization.
Experience collaborating with researchers to scale experimental ideas.
Ability to thrive in a fast-paced environment and iterate rapidly from experimentation to production.
Bachelor’s degree or an equivalent combination of education, training, and/or experience.
A field of study relevant to the role, demonstrated through coursework, training, or professional experience.

Nice To Haves

Experience with language model training infrastructure and distributed ML frameworks such as PyTorch or JAX.
Background building infrastructure for AI research labs or large-scale ML organizations.
Knowledge of GPU/TPU architectures and language model inference optimization.
Experience with AWS or GCP at enterprise scale.
Familiarity with VM and container orchestration.
Experience with workflow orchestration tools and experiment management systems.
A history of working with large-scale reinforcement learning.
Comfort with large-scale data pipelines using tools such as Beam, Spark, or Dask.

Responsibilities

Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments.
Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities.
Develop robust and reliable evaluation frameworks for measuring progress toward scientific AGI.
Build scalable and performant VM, sandboxing, and 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 for advanced language model training requirements.
Optimize large-scale training and inference pipelines for stable and efficient reinforcement learning.