Training Performance Engineer

About the Position

Join OpenAI as a Training Performance Engineer to optimize distributed machine learning training runtimes and improve model training efficiency.

Location

• City: San Francisco
• Country: United States
• Remote: No

Employment Type

• Type: Full-time
• Work Location Type: Hybrid

Compensation

• Salary Range: $295K – $500K
• Offers: Equity

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.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

Training Runtime designs the core distributed machine-learning training runtime that powers everything from early research experiments to frontier-scale model runs. With a dual mandate to accelerate researchers and enable frontier scale, we’re building a unified, modular runtime that meets researchers where they are and moves with them up the scaling curve.

Our work focuses on three pillars:

• High-performance, asynchronous, zero-copy tensor and optimizer-state-aware data movement
• Performant, high-uptime, fault-tolerant training frameworks (training loop, state management, resilient checkpointing, deterministic orchestration, and observability)
• Distributed process management for long-lived, job-specific and user-provided processes

We integrate proven large-scale capabilities into a composable, developer-facing runtime so teams can iterate quickly and run reliably at any scale, partnering closely with model-stack, research, and platform teams. Success for us is measured by raising both training throughput (how fast models train) and researcher throughput (how fast ideas become experiments and products).

About the Role

As a Training Performance Engineer, you’ll drive efficiency improvements across our distributed training stack. You’ll analyze large-scale training runs, identify utilization gaps, and design optimizations that push the boundaries of throughput and uptime. This role blends deep systems understanding with practical performance engineering - analyzing GPU kernel performance, collective communication throughput, investigating I/O bottlenecks, and sharding our models so we can train them at massive scale.

You’ll help ensure that our clusters are running at peak performance, enabling OpenAI to train larger, more capable models with the same compute budget.

Responsibilities

• Profile end-to-end training runs to identify performance bottlenecks across compute, communication, and storage.
• Optimize GPU utilization and throughput for large-scale distributed model training.
• Collaborate with runtime and systems engineers to improve kernel efficiency, scheduling, and collective communication performance.
• Implement model graph transforms to improve end-to-end throughput.
• Build tooling to monitor and visualize MFU, throughput, and uptime across clusters.
• Partner with researchers to ensure new model architectures scale efficiently during pre-training.
• Contribute to infrastructure decisions that improve reliability and efficiency of large training jobs.

Requirements

• Core Skills:

  • Strong programming skills in Python and C++ (Rust or CUDA a plus).
  • Experience running distributed training jobs on multi-GPU systems or HPC clusters.
  • Exposure to frameworks like PyTorch, JAX, or TensorFlow and an understanding of how large-scale training loops are built.
  • Comfortable collaborating across teams and translating raw profiling data into practical engineering improvements.

• Nice to Have:

  • Familiarity with NCCL, MPI, or UCX communication libraries.
  • Experience with large-scale data loading and checkpointing systems.
  • Prior work on training runtime, distributed scheduling, or ML compiler optimization.

About the Company

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.

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.

For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates.

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We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.

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