Job description

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); and 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. This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees. In this role, you will: - 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. You might thrive in this role if you: - Love optimizing performance and digging into systems to understand how every layer interacts. - Have strong programming skills in Python and C++ (Rust or CUDA a plus). - Have experience running distributed training jobs on multi-GPU systems or HPC clusters. - Enjoy debugging complex distributed systems and measuring efficiency rigorously. - Have exposure to frameworks like PyTorch, JAX, or TensorFlow and an understanding of how large-scale training loops are built. - Are 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 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. 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 cdn.openai.com. 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. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form form.asana.com. No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link form.asana.com. OpenAI Global Applicant Privacy Policy cdn.openai.com At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

Requirements

Must-haves

About the Team
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.
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.
You might thrive in this role if you:
Love optimizing performance and digging into systems to understand how every layer interacts.
Have experience running distributed training jobs on multi-GPU systems or HPC clusters.
Enjoy debugging complex distributed systems and measuring efficiency rigorously.
Have exposure to frameworks like PyTorch, JAX, or TensorFlow and an understanding of how large-scale training loops are built.
Are comfortable collaborating across teams and translating raw profiling data into practical engineering improvements.

Nice-to-haves

Have strong programming skills in Python and C++ (Rust or CUDA a plus).
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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

pythonrustmlai

Platform: ashby
Effort: quick