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

As a Technical Program Manager on the Compute team, you will help drive the planning, coordination, and execution of programs that keep Anthropic's compute infrastructure running efficiently at scale. 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. The exact focus will depend on your strengths and the team's evolving needs.

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

Compensation

Annual Salary: $290,000 - $365,000 USD

Anthropic offers competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Anthropic is a public benefit corporation headquartered in San Francisco.

Logistics

• Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
• Location-based hybrid policy: Currently, Anthropic expects all staff to be in one of its offices at least 25% of the time. Some roles may require more time in the offices.
• Visa sponsorship: Anthropic does sponsor visas, but it is not able to successfully sponsor visas for every role and every candidate. If an offer is made, Anthropic will make every reasonable effort to get the candidate a visa, and it retains an immigration lawyer to help with this.

Anthropic encourages you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so you are urged not to exclude yourself prematurely and to submit an application if you're interested in this work. Anthropic thinks AI systems like the ones it is building have enormous social and ethical implications, believes this makes representation even more important, and strives to include a range of diverse perspectives on its team.

Your safety matters

To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, Anthropic may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links. Visit anthropic.com/careers directly for confirmed position openings.

How we're different

Anthropic believes that the highest-impact AI research will be big science. It works as a single cohesive team on just a few large-scale research efforts, and values impact, advancing its long-term goals of steerable, trustworthy AI, rather than work on smaller and more specific puzzles. Anthropic views AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. It is an extremely collaborative group, and hosts frequent research discussions to ensure that it is pursuing the highest-impact work at any given time. As such, it greatly values communication skills.

The easiest way to understand Anthropic's research directions is to read its recent research. This research continues many of the directions the team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Guidance on Candidates' AI Usage

Learn about Anthropic's policy for using AI in its application process at https://www.anthropic.com/candidate-ai-guidance.

How to apply

Deadline to apply: None, applications will be received on a rolling basis.

Cloud platforms

• AWS
• Google Cloud (GCP)
• Microsoft Azure
• Hybrid cloud/on-premises environments

Job scheduling and orchestration

• Kubernetes
• Slurm
• Borg
• YARN
• Custom schedulers

Accelerator and ML infrastructure

• GPU or accelerator infrastructure for large-scale ML training and inference workloads

Observability

• Dashboards
• Alerting
• Efficiency metrics
• Cost attribution