Staff Infrastructure Engineer, Discovery Team

Details

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the Team

Our team is organized around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific frontier. Our team likes to think across the whole model stack. Currently the team is focused on improving models' abilities to use computers – as a laboratory for long horizon tasks and a key blocker to many scientific workflows.

About the role

As a Staff Infrastructure Engineer on our team you will work end to end, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with performance optimization, distributed systems, vm/sandboxing/container deployment, and large scale data pipelines. Familiarity with language model training, evaluation, and inference is highly encouraged.

Join us in our mission to develop advanced AI systems that are both powerful and beneficial for humanity.

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 towards scientific AGI
• Build scalable and performant 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

Requirements

• 6+ years of highly relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems
• Strong communicator and able to work collaboratively
• Deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads
• Experience with containerization technologies (Docker, Kubernetes) and orchestration at scale
• Proven track record of building large-scale data pipelines and distributed storage systems
• Excellent at diagnosing and resolving complex infrastructure challenges in production environments
• Able to work effectively across the full ML stack from data pipelines to performance optimization
• Experience collaborating with researchers to scale experimental ideas

Strong candidates may also have

• Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.)
• Background in building infrastructure for AI research labs or large-scale ML organizations
• Knowledge of GPU/TPU architectures and language model inference optimization
• Experience with cloud platforms (AWS, GCP) at enterprise scale
• Familiarity with VM and container orchestration
• Experience with workflow orchestration tools and experiment management systems
• History working with large scale reinforcement learning
• Comfort with large scale data pipelines (Beam, Spark, Dask, …)

Compensation

Annual Salary: $340,000 - $425,000 USD

Our total compensation package for full-time employees includes equity, benefits, and may include incentive compensation.

Logistics

• Education requirements: At least a Bachelor's degree in a related field or equivalent experience.
• Location-based hybrid policy: We expect all staff to be in one of our offices at least 25% of the time. Some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas, and we make every reasonable effort to help secure visas for candidates we hire.

How we're different

We believe the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. We value impact and collaboration, and host frequent research discussions. We view AI research as an empirical science with ties to physics and biology as well as computer science.

Come work with us

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a collaborative office space. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.