About Anthropic

• 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 Role

• Building and maintaining critical systems that serve Claude to millions of users worldwide.
• Maximizing compute efficiency and enabling breakthrough research.
• Tackling complex, distributed systems challenges across multiple accelerator families and emerging AI hardware in diverse cloud platforms.

Strong Candidates May Also Have Experience With

• High-performance, large-scale distributed systems.
• Implementing and deploying machine learning systems at scale.
• Load balancing, request routing, or traffic management systems.
• LLM inference optimization, batching, and caching strategies.
• Kubernetes and cloud infrastructure (AWS, GCP).
• Python or Rust.

You May Be a Good Fit If You

• Have significant software engineering experience, particularly with distributed systems.
• Are results-oriented, with a bias towards flexibility and impact.
• Pick up slack, even if it goes outside your job description.
• Want to learn more about machine learning systems and infrastructure.
• Thrive in environments where technical excellence directly drives both business results and research breakthroughs.
• Care about the societal impacts of your work.

Representative Projects Across the Organization

• Designing intelligent routing algorithms optimizing request distribution across thousands of accelerators.
• Autoscaling the compute fleet to match supply with demand across various workloads.
• Building deployment pipelines for releasing new models to millions of users.
• Integrating new AI accelerator platforms to maintain hardware-agnostic competitive advantage.
• Contributing to new inference features like structured sampling and prompt caching.
• Supporting inference for new model architectures.
• Analyzing observability data to tune performance based on real-world production workloads.
• Managing multi-region deployments and geographic routing for global customers.

Perks and Benefits

• Competitive compensation and benefits.
• Optional equity donation matching.
• Generous vacation and parental leave.
• Flexible working hours.
• Lovely office space for collaboration with colleagues.