Our compute fleet is the foundation on which every model training run, evaluation, and inference workload depends. You'll join a small, high-impact TPM team and take ownership of critical workstreams across the compute lifecycle, from how supply is procured and brought online, to how capacity is allocated and utilized across teams.

You'll partner with Infrastructure, Systems, Research, Finance, and Capacity Engineering to shape the processes, tooling, and coordination mechanisms that allow Anthropic to move fast while managing an increasingly complex compute environment.

Responsibilities:

• Own and drive critical programs across the compute lifecycle, coordinating execution across multiple engineering, research, and operations teams
• Build and maintain operational visibility into the compute fleet, ensuring the organization has a clear picture of supply, demand, utilization, and health
• Lead cross-functional coordination for compute transitions: bringing new capacity online, migrating workloads, and managing decommissions across cloud providers and hardware platforms
• Partner with engineering and research leadership to navigate competing priorities and drive alignment on how compute resources are planned, allocated, and used
• Identify and close operational gaps across the compute pipeline, whether through new tooling, improved processes, or better cross-team communication
• Own trade-off discussions between utilization, cost, latency, and reliability, synthesizing inputs from technical and business stakeholders and communicating decisions to leadership
• Develop and improve the processes and frameworks the team uses to plan, track, and execute compute programs at increasing scale and complexity

You may be a good fit if you:

• Have 7+ years of technical program management experience in infrastructure, platform engineering, or compute-intensive environments
• Have led complex, cross-functional programs involving multiple engineering teams with competing priorities and ambiguous requirements
• Have experience working with research or ML teams and translating their needs into operational plans and technical requirements
• Are comfortable diving deep into technical details (cloud infrastructure, cluster management, job scheduling, resource orchestration) while maintaining program-level visibility
• Thrive in ambiguous, fast-moving environments where you need to define scope and build processes from the ground up
• Have strong communication skills and can engage credibly with engineers, researchers, finance, and executive leadership
• Have a track record of building trust with engineering teams and driving changes through influence rather than authority

Strong candidates may also have:

• Experience managing compute capacity across multiple cloud providers (AWS, GCP, Azure) or hybrid cloud/on-premises environments
• Familiarity with job scheduling, resource orchestration, or workload management systems (Kubernetes, Slurm, Borg, YARN, or custom schedulers)
• Experience with GPU or accelerator infrastructure, including the unique challenges of large-scale ML training and inference workloads
• Built or improved observability for infrastructure systems: dashboards, alerting, efficiency metrics, or cost attribution
• Capacity planning experience including demand forecasting, cost modeling, or hardware lifecycle management
• Scaled through hypergrowth in AI/ML, HPC, or large-scale cloud environments

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Our compute fleet is the foundation on which every model training run, evaluation, and inference workload depends. You'll join a small, high-impact TPM team and take ownership of critical workstreams across the compute lifecycle, from how supply is procured and brought online, to how capacity is allocated and utilized across teams.

You'll partner with Infrastructure, Systems, Research, Finance, and Capacity Engineering to shape the processes, tooling, and coordination mechanisms that allow Anthropic to move fast while managing an increasingly complex compute environment.

Responsibilities:

• Own and drive critical programs across the compute lifecycle, coordinating execution across multiple engineering, research, and operations teams
• Build and maintain operational visibility into the compute fleet, ensuring the organization has a clear picture of supply, demand, utilization, and health
• Lead cross-functional coordination for compute transitions: bringing new capacity online, migrating workloads, and managing decommissions across cloud providers and hardware platforms
• Partner with engineering and research leadership to navigate competing priorities and drive alignment on how compute resources are planned, allocated, and used
• Identify and close operational gaps across the compute pipeline, whether through new tooling, improved processes, or better cross-team communication
• Own trade-off discussions between utilization, cost, latency, and reliability, synthesizing inputs from technical and business stakeholders and communicating decisions to leadership
• Develop and improve the processes and frameworks the team uses to plan, track, and execute compute programs at increasing scale and complexity

You may be a good fit if you:

• Have 7+ years of technical program management experience in infrastructure, platform engineering, or compute-intensive environments
• Have led complex, cross-functional programs involving multiple engineering teams with competing priorities and ambiguous requirements
• Have experience working with research or ML teams and translating their needs into operational plans and technical requirements
• Are comfortable diving deep into technical details (cloud infrastructure, cluster management, job scheduling, resource orchestration) while maintaining program-level visibility
• Thrive in ambiguous, fast-moving environments where you need to define scope and build processes from the ground up
• Have strong communication skills and can engage credibly with engineers, researchers, finance, and executive leadership
• Have a track record of building trust with engineering teams and driving changes through influence rather than authority

Strong candidates may also have:

• Experience managing compute capacity across multiple cloud providers (AWS, GCP, Azure) or hybrid cloud/on-premises environments
• Familiarity with job scheduling, resource orchestration, or workload management systems (Kubernetes, Slurm, Borg, YARN, or custom schedulers)
• Experience with GPU or accelerator infrastructure, including the unique challenges of large-scale ML training and inference workloads
• Built or improved observability for infrastructure systems: dashboards, alerting, efficiency metrics, or cost attribution
• Capacity planning experience including demand forecasting, cost modeling, or hardware lifecycle management
• Scaled through hypergrowth in AI/ML, HPC, or large-scale cloud environments

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The ideal candidate will have 5+ years of hands-on experience in web frontend development, with a strong preference for React-based applications. They will also have 2+ years leading and managing a high-performing engineering team. A passion for infrastructure, tooling, developer productivity, or performance optimisation and the ability to drive technical initiatives that improve overall team efficiency and code quality is essential.

The salary range for this role is $182,400-$247,000 USD.

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