Compute Efficiency Engineer, Anthropic

• Have 6+ years of relevant industry experience, 1+ year leading large scale, complex projects or teams as an engineer or tech lead
• Deep expertise in distributed systems at scale, with a strong focus on infrastructure reliability, scalability, and continuous improvement
• Strong proficiency in at least one programming language (e.g., Python, Rust, Go, Java)
• Hands-on experience with cloud infrastructure, including Kubernetes, Infrastructure as Code, and major cloud providers such as AWS or GCP
• Experience optimizing end-to-end performance of distributed systems, including workload right-sizing and resource utilization tuning
• You possess a deep curiosity for how things work under the hood and have a proven ability to work independently to solve opaque performance issues
• Experience designing or working with performance and utilization monitoring tools in large-scale, distributed environments
• Strong problem-solving skills with the ability to work independently and navigate ambiguity
• Excellent communication and collaboration skills—you will work closely with internal and external stakeholders to build consensus and drive projects forward
• We encourage you to apply even if you do not believe you meet every single qualification

Desirable

• You will work across the full infrastructure stack, from cloud platforms and networking to application-level performance, and will bridge the gap between high-level research needs and low-level hardware constraints to build the most efficient AI infrastructure in the world
• You will help with building the telemetry, cost attribution, and optimization frameworks that ensure every dollar of our infrastructure investment delivers maximum value
• This is a high-impact, cross-functional role at the intersection of systems engineering, financial optimization, and AI infrastructure
• Build and evolve telemetry and monitoring systems to provide deep visibility into infrastructure performance, utilization, and costs across our cloud and datacenter fleets
• Design and implement cost attribution frameworks for our multi-tenant infrastructure, enabling teams to understand and optimize their resource consumption
• Identify and resolve performance bottlenecks and capacity hotspots through deep analysis of distributed systems at scale
• Partner closely with cloud service providers and internal stakeholders to optimize cluster configurations, workload placement, and resource utilization across AI training and inference workloads—including large-scale clusters spanning thousands to hundreds of thousands of machines
• Develop and champion engineering practices around efficiency, driving a culture of performance awareness and cost-conscious design across Anthropic
• Collaborate with research and product teams to deeply understand their infrastructure needs, and design solutions that balance performance with cost efficiency
• Drive architectural improvements and code-level optimizations across multiple services and platforms to deliver measurable utilization and performance gains