Technical Program Manager (tpm), Infrastructure

Cursor Cursor · Coding AI · San Francisco, CA · Engineering

Technical Program Manager (TPM) to partner with Infrastructure and ML teams to drive COGS attribution, R&D spend efficiency, and resource allocation across Cursor's infrastructure. This role partners with engineering leaders in ML, Infrastructure, and Finance to turn complex cost and capacity data into clear decisions, then drives the programs that land those decisions. Owns programs for GPU allocation, inference cost management, infrastructure spend attribution, and R&D investment efficiency.

What you'd actually do

  1. Own COGs and R&D attribution programs. Stand up cost attribution across inference, compute, and infrastructure workloads. Partner with Finance and Data to connect spend to products, features, and business metrics. Make cost data actionable for engineering and executive decision-making.
  2. Drive GPU and infrastructure resource allocation. Build frameworks for capacity planning and allocation across 1P models, 3P inference, and experimentation. Run recurring reviews with ML and Infra leadership. Translate business priorities into concrete allocation plans.
  3. Manage technical programs with infrastructure teams. Scope and drive programs in cost optimization, capacity planning, reliability, and migration. Own the operating rhythm for each program alongside a designated sponsor: track progress, escalate blockers, ensure outcomes.
  4. Partner strategically with engineering leaders. Serve as a thought partner on resource tradeoffs, investment prioritization, and operational improvements across ML, Infrastructure, and Finance. Synthesize complex technical and financial information into clear recommendations.
  5. Roll up your sleeves. Dig into dashboards, data, and systems. Write the doc, build the model, run the analysis, and get stuff done.

Skills

Required

  • 5+ years in Technical Program Management or similar roles in highly technical environments.
  • Experience with cloud infrastructure costs, GPU/compute capacity planning, or COGs/R&D attribution.
  • Strong analytical skills; comfortable with cost data, utilization metrics, and financial models.
  • Proven ability to drive cross-functional programs across Engineering, ML, and Finance without direct authority.
  • Bias toward action and outcomes over process and ceremony.

What the JD emphasized

  • GPU allocation
  • inference cost management
  • infrastructure spend attribution
  • R&D investment efficiency
  • cloud infrastructure costs
  • GPU/compute capacity planning
  • COGs/R&D attribution