Sr./Staff TPM - Inference Capacity

About the role

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

Key Responsibilities

• Run weekly capacity planning and daily capacity and deployment tracking with Engineering, product and operations team.
• Own fleet utilization reporting and forecasting
• Drive capacity planning for new customer deployments and major model launches
• Drive continuous improvement and stakeholder adoption of new capacity management platform
• Lead planning around major infrastructure events including but not limited to new customer commits, new model releases, change to DC/cluster architecture, etc. that impacts capacity and fleet utilization
• Update capacity plans and forecasts accordingly
• Maintain Jira EPICs and Confluence pages related to capacity planning, reporting and change management to ensure execution transparency across teams

Qualifications

• 5+ years of TPM, technical program management, or product operations experience in cloud infrastructure, large-scale ML serving, or hyperscaler capacity planning
• Experience leading large cross-functional programs involving Engineering, Product, and Operations
• Comfort with the inference serving stack: model replicas, batching, prefill/decode, KV cache, accelerator scheduling
• Strong data fluency: SQL, Grafana, basic Python or Flux to pull your own numbers without waiting for an analyst
• Track record of running a recurring cross-functional ritual involving senior engineers and LT
• Direct experience with AI accelerator fleet operations such as Habana, TPU pods, Inferentia, Trainium