Staff Software Engineer, Anthropic

• Experience instrumenting ML workloads for performance monitoring/efficiency
• Experience with high performance, large scaled distributed systems
• Experience with LLM inference and Reinforcement Learning
• Observability tooling and best practices (logging, metrics, tracing)
• 10+ years experience in capacity efficiency or performance engineering
• 10+ years experience in a technical role
• Have experience in scripting and building automation tools
• Are self-disciplined and thrives in fast paced environments
• Have Excellent communication skills
• Pick up slack, even if it goes outside your job description
• Have attention to detail and a passion for correctness

Desirable

• The mission of the Capacity Efficiency & Performance team is to provide input into our company-wide cloud infrastructure strategy and efficiency deliverables, support and improve our model development through improved observability and capacity efficiency, advise on key decisions affecting budget, provide capacity planning and performance expertise to various Anthropic-wide stakeholders in both finance and engineering leadership
• You will be expected to work with engineering teams to ensure optimal operation and growth of our infrastructure from both a cost and technology perspective, with research engineering to scope and understand the observability and capacity needs for model development, and collaborate cross-functionally with finance and data science partners to analyze and forecast growth
• Develop self-service tools and dashboards to enable Anthropic engineers to understand their capacity, efficiency, and costs, leveraging observability best practices
• Design ML-informed forecasting models and automation to help capacity plan for both near and long-term outcomes
• Institute governance workflows and optimization pipelines for managing cloud resources across LLM training and inference workloads
• Investigate capacity requests and recommend right-sizing strategies for performance optimization across multiple platforms/environments
• Build comprehensive cost-to-serve analytics programs that account for ML/LLM-specific infrastructure requirements and inform pricing strategies
• Lead technical partnerships with cloud providers and hardware vendors to optimize capacity utilization aligned with ML workload patterns
• Design and implement observability solutions that provide insights into infrastructure efficiency for large-scale distributed systems
• Collaborate with engineering teams to identify and resolve performance bottlenecks in Kubernetes-based ML infrastructure
• Partner with research teams to quantify computational requirements for new ML initiatives and develop appropriate capacity plans

• Deadline to apply: None. Applications will be reviewed on a rolling basis