Compensation
The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.
- Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
- Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
- 401(k) retirement plan with employer match
- Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
- Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
- 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
- Mental health and wellness support
- Employer-paid basic life and disability coverage
- Annual learning and development stipend to fuel your professional growth
- Daily meals in our offices, and meal delivery credits as eligible
- Relocation support for eligible employees
- Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.
About The Team
The team works on research and systems that advance frontier models. Our work often goes beyond standard training recipes, which means we also build the infrastructure needed to make new training approaches practical at scale. This is a team where systems work is directly tied to research progress: better tools, abstractions, and runtimes can unlock experiments that would otherwise be too slow, brittle, or difficult to express.
About The Role
This is a systems engineering role focused on ML training infrastructure. You will work on the systems layer that turns novel research ideas into runnable, measurable training workloads for large models. The work can sit on the critical path for model releases, bringing both the excitement of direct impact and the responsibility of building systems that remain reliable under real pressure.
In This Role, You Will
- Build and maintain infrastructure for large-scale model training and experimentation.
- Design APIs and interfaces that make complex training workflows easier to express and harder to misuse.
- Improve reliability, debuggability, and performance across training and data pipelines.
- Debug issues spanning Python, PyTorch, distributed systems, GPUs, networking, and storage.
- Write tests, benchmarks, and diagnostics that catch meaningful regressions.
You Might Thrive In This Role If You
- You want to build systems that enable new model training approaches, not just optimize established ones.
- You have strong systems instincts and care deeply about performance, reliability, and clean abstractions.
- You have good taste in API and interface design, with empathy for the researchers and engineers using your tools.
- You are comfortable working across ML research code and production-quality infrastructure.
- You enjoy debugging from evidence: profiles, traces, logs, tests, and minimal reproductions.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
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