Compute Optimization Researcher/Engineer

About the Role

As a key member of the Compute Optimization team, you will work on high-impact optimization problems spanning capacity allocation, demand forecasting, cluster planning, workload placement, and infrastructure utilization. Your expertise in mathematical modeling, software systems, and cross-functional execution will be essential in improving how compute is planned and consumed across GPU clusters, networking, storage, and data center environments.

What You Will Do

• 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

What We Are Looking For

• 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 Have

• 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

Benefits and Perks

• Competitive salary and benefits package
• Opportunity to work on high-impact problems in compute optimization
• Collaborative and dynamic work environment
• Flexible working hours and remote work options
• Access to cutting-edge technologies and tools
• Professional development and growth opportunities
• Recognition and rewards for outstanding performance

• To stand out in this role, focus on showcasing your expertise in mathematical modeling, software systems, and cross-functional execution.
• Be prepared to back up your claims with concrete examples of optimization models and planning systems you've developed in the past.
• Familiarize yourself with Openai's technology stack and be prepared to discuss how you can contribute to the company's mission.
• Highlight your ability to communicate complex technical ideas to non-technical stakeholders, and be prepared to provide examples of times when you've done so effectively.
• Consider creating a portfolio of your work, including examples of optimization models, forecasting frameworks, and decision tools you've developed.
• When negotiating salary, be prepared to discuss your expectations and provide evidence of your market value.
• Be wary of red flags such as unclear expectations, lack of resources, or unrealistic deadlines, and don't be afraid to ask questions during the interview process.