Technical Program Manager, Inference

San Francisco, CA | New York City, NY | Seattle, WA · San Francisco · USD 290,000-435,000 / year · 7+ years experience · Hybrid · Posted Oct 15, 2026

At a glance

Experience: 7+ years experience
Workplace: Hybrid
Location: San Francisco, CA | New York City, NY | Seattle, WA · San Francisco
Salary: USD 290,000-435,000 / year · Base salary
Visa sponsorship: Visa sponsorship available

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.

Anthropic is looking for a Technical Program Manager to drive the programs that scale its inference organization, serving Claude to millions of users and enterprise customers.
The role spans scaling and infrastructure, developer velocity, the inference engine, and performance across accelerator and cloud platforms.

Own the programs that scale Claude’s serving fleet across accelerator and cloud platforms, from early direction through delivery.
Drive new capacity online end to end, working across compute, infrastructure, security, networking, and inference teams so one person is accountable for the whole path.
Build visibility into supply, demand, and utilization with capacity and finance partners, and drive trade-offs among utilization, cost, latency, and reliability to a clear decision.
Lead reliability programs and large migrations without disrupting production traffic or research, and make sure incident follow-ups land in team roadmaps.
Drive the programs that make inference engineers faster: define the productivity metrics that matter, find the bottlenecks, and improve how software is built, tested, and released across platforms.
Coordinate roadmaps and priorities across the production, research, and safeguards needs that depend on the same teams.
Track performance and efficiency work from opportunity to measured impact in production, with correctness held as a release criterion.
Own inference readiness for model launches alongside launch TPMs.

7+ years of experience as a TPM, or in an equivalent engineering or SRE lead role, owning large multi-team infrastructure programs end to end in production distributed systems, ML infrastructure, or cloud platforms.
Technical fluency in large-scale serving infrastructure, including request routing and load balancing, autoscaling, capacity planning, cluster orchestration, deployment and rollout, and observability.
A working understanding of how LLM inference uses hardware (prefill and decode, batching, caching, compute versus memory bandwidth versus interconnect), or a track record of getting deep in an unfamiliar technical domain quickly.
A track record of building program mechanisms from scratch on ambiguous, fast-moving efforts, and of retiring process that has stopped earning its keep.
The ability to drive outcomes across organizations you don't manage, with competing priorities and unclear decision ownership.
Strong written communication, including plans, reviews, and executive updates that many teams depend on.
Low ego and high ownership, caring more about the outcome than about scope or credit.
Care about Anthropic's mission and about serving frontier AI safely and reliably.

Direct experience with LLM or ML inference serving at scale, such as model servers, batching and scheduling, KV caching, quantization, speculative decoding, or multi-model routing.
Experience with accelerator infrastructure (GPU, TPU, or Trainium), including cluster bring-up, hardware qualification, or running one workload across more than one accelerator platform.
Experience managing capacity across multiple clouds or regions, including demand forecasting, headroom policy, cost modeling, or unit economics for compute-heavy services.
Experience with developer productivity, release engineering, or CI/CD at scale, especially for hardware-dependent workloads, and with using engineering metrics to measurably raise velocity.
Experience running performance or efficiency programs, with enough grounding in rooflines, kernels, ML compilers (XLA, Triton, Neuron), numerics, or collective communication to reason about trade-offs.
Familiarity with Kubernetes-based orchestration and job scheduling.
Experience working with research teams and turning their needs into infrastructure requirements.
Practical experience using AI tools in your own workflow, including building automations or agents, and clear ideas about where they help and where they don't.

Location-based hybrid policy requiring all staff to be in one of the offices at least 25% of the time, with some roles potentially requiring more time in offices.