Technical Program Manager at Cursor driving infrastructure cost attribution, GPU allocation, and R&D spend efficiency. Partners with ML, Infrastructure, and Finance teams to translate cost data into engineering decisions and manage cross-functional programs.
Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.
Cursor is hiring a Technical Program Manager (TPM) to partner with our Infrastructure 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.
You'll own the programs to enable Cursor to achieve its top business priorities that affect Engineering prioritization, working with an executive or engineering sponsor to scope, drive, and deliver cross-functional initiatives spanning GPU allocation, inference cost management, infrastructure spend attribution, and R&D investment efficiency.
What success looks like
COGs and R&D spend are clearly attributed, understood, and actionable across Engineering and Finance.
GPU and infrastructure allocation decisions are grounded in data and aligned to business priorities.
Cost and efficiency programs run on a predictable cadence with measurable outcomes.
Engineering leaders trust the TPM as a strategic partner who removes ambiguity and drives execution. </aside>
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.
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.
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.
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.
Roll up your sleeves. Dig into dashboards, data, and systems. Write the doc, build the model, run the analysis, and get stuff done.
Stand up COGs attribution: mapping inference and infrastructure spend to products and workloads, with dashboards used by engineering and finance.
Build a GPU allocation review that runs bi-weekly with ML and Infra leadership, producing clear decisions tied to business priorities.
Drive an R&D spend efficiency program identifying the highest-leverage cost reductions across engineering.
Scope a capacity forecasting model connecting product growth to infrastructure investment needs for planning.
Thinks in systems: connects infrastructure decisions to business outcomes.
Can operate at every level. Equally comfortable in a strategy discussion and a spreadsheet.
Builds trust with senior technical leaders by being prepared, direct, and reliable.
Operates with high ownership. Doesn't wait to be told what to do.
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.