Compute Optimization Researcher/Engineer

OpenAI•San Francisco, CA

1d•Hybrid

About The Position

OpenAI’s infrastructure organization builds and operates the systems that power frontier AI workloads at global scale. As our compute footprint expands across first-party data centers, cloud providers, and strategic partners, efficient capacity planning and resource allocation become critical to delivering reliable and cost-effective compute. The Compute Optimization team sits at the intersection of engineering, operations, finance, and infrastructure strategy. We develop the models, decision systems, and planning frameworks that optimize how compute resources are deployed, scheduled, and scaled across a rapidly growing global environment. We are seeking Compute Optimization Researcher/Engineer to build the systems that maximize the value of OpenAI’s global compute capacity. In this role, you will work on high-impact optimization problems spanning capacity allocation, demand forecasting, cluster planning, workload placement, and infrastructure utilization. You will combine mathematical modeling, software systems, and cross-functional execution to improve how compute is planned and consumed across GPU clusters, networking, storage, and data center environments. This role is ideal for candidates with backgrounds in operations research, optimization, applied math, infrastructure systems, or large-scale capacity planning. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance.

Requirements

Doctorate degree in Computer Science, Engineering, Mathematics, Operations Research, Economics, or related field.
5+ years of experience in optimization, planning, infrastructure analytics, or systems engineering.
Strong experience with linear programming, mixed-integer optimization, convex optimization, simulation, or forecasting methods.
Proficiency in Python and data tooling (SQL, Pandas, Spark, etc.).
Experience translating real-world business constraints into scalable optimization systems.
Strong analytical problem-solving skills with comfort operating in ambiguous environments.
Ability to influence cross-functional stakeholders without formal authority.
Excellent communication skills with both technical and non-technical audiences.

Nice To Haves

Experience with large-scale infrastructure, cloud capacity planning, or data center operations.
Familiarity with tools such as Gurobi, CPLEX, CVXPY, Pyomo, or similar solvers.
Experience optimizing GPU fleets, networking systems, or distributed compute environments.
Background in supply-demand planning, logistics, marketplace optimization, or resource scheduling.
Experience working in fast-scaling technology environments.

Responsibilities

Build optimization models for compute allocation, workload scheduling, and cluster utilization.
Develop planning systems that balance supply, demand, cost, latency, and reliability constraints.
Create forecasting frameworks for GPU demand, infrastructure growth, and capacity needs.
Design decision tools for allocating compute across internal teams, products, and strategic priorities.
Partner with architecture, infrastructure engineering, finance, and operations teams to translate business needs into mathematical models.
Integrate multiple operational data sources into planning systems and optimization workflows.
Improve utilization of GPUs, networking, power, cooling, and storage infrastructure.
Analyze tradeoffs across first-party data centers, cloud providers, and hybrid environments.
Build dashboards, metrics, and operational tooling for capacity decision-making.
Lead ambiguous, cross-functional initiatives that improve infrastructure efficiency at scale.
Present recommendations clearly to technical leaders and executives.
Continuously refine models based on changing workloads, supply constraints, and business priorities.