Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI
Data Analysis @ 4
Data Science @ 6
Distributed Systems
Experimentation @ 7
GPU
Machine Learning
Mathematics @ 4
Python @ 7
Reinforcement Learning
SQL @ 7
Statistics @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
OpenAI’s Industrial Compute organization is responsible for ensuring compute infrastructure scales efficiently to support millions of users and increasingly sophisticated AI models.
This role partners with Capacity Systems Engineering, Infrastructure, Product, and Research to optimize inference capacity across OpenAI’s global GPU fleet. It combines statistical modeling, large-scale data analysis, forecasting, and systems thinking to inform infrastructure investments, performance-efficiency trade-offs, and customer experience.
Responsibilities
- Build statistical and machine learning models to profile and improve GPU utilization, latency, throughput, and overall fleet efficiency.
- Develop forecasting models for inference demand across products, regions, and model families.
- Analyze production workloads to identify latency bottlenecks and capacity constraints, highlighting optimization opportunities.
- Partner with Capacity Systems Engineering to inform infrastructure planning and long-term GPU investment strategies.
- Design experiments and simulations to evaluate scheduling policies, serving strategies, and infrastructure trade-offs.
- Build dashboards and operational metrics that enable leadership to make data-driven capacity decisions.
- Collaborate with Product, Research, Finance, and Infrastructure teams to align compute planning with business growth and model roadmaps.
- Communicate technical findings clearly to engineering teams and executive leadership.
Requirements
- MS or PhD in Statistics, Computer Science, Operations Research, Applied Mathematics, Economics, or a related quantitative discipline, or equivalent industry experience.
- 5+ years of experience working in infrastructure data science.
- Strong expertise in Python and SQL.
- Experience building forecasting, optimization, or predictive models.
- Strong understanding of experimentation, statistical inference, and causal analysis.
- Experience communicating analytical insights to executive stakeholders.
Preferred Skills
- Capacity planning
- Distributed systems
- AI infrastructure
- Datacenter design and buildout
- Queueing theory
- Time-series forecasting
- Operations research
- Supply-demand modeling
- Reinforcement learning for resource allocation
- Cost optimization
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. OpenAI is an equal opportunity employer and does not discriminate on the basis of legally protected characteristics. The company is committed to providing reasonable accommodations to applicants with disabilities. Background checks are administered in accordance with applicable law.
Benefits
- Medical, dental, and vision insurance for employees and their families, with employer contributions to Health Savings Accounts.
- Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses.
- 401(k) retirement plan with employer match.
- Paid parental, medical, and caregiver leave.
- Paid time off and paid company holidays.
- Mental health and wellness support.
- Employer-paid basic life and disability coverage.
- Annual learning and development stipend.
- Daily office meals and eligible meal delivery credits.
- Relocation support for eligible employees.
- Additional taxable fringe benefits may be provided, including charitable donation matching and wellness stipends.
Total compensation includes the listed base salary, equity, and performance-related bonuses for eligible employees.