培训性能工程师
Training Performance Engineer
关于团队
Training Runtime 设计核心的分布式机器学习训练运行时，从早期研究实验到前沿规模模型运行都由其驱动。在加速研究人员和实现前沿规模的双重使命下，我们正在构建一个统一、模块化的运行时，满足研究人员当前的需求，并随着他们逐步提升扩展能力。
我们的工作聚焦于三大支柱：高性能、异步、零拷贝张量和优化器状态感知的数据传输；高性能、高可用性、容错的训练框架（训练循环、状态管理、弹性检查点、确定性编排和可观测性）；以及针对长期运行、特定任务和用户提供的进程的分布式进程管理。
我们将经过验证的大规模能力整合到可组合、面向开发者的运行时中，使团队能够快速迭代并在任何规模下可靠运行，与模型栈、研究和平台团队紧密合作。对我们而言，成功意味着提升训练吞吐量（模型训练速度）和研究人员吞吐量（想法转化为实验和产品的速度）。
关于职位
作为 Training Performance Engineer，你将推动我们分布式训练堆栈的效率提升。你将分析大规模训练运行，识别利用率差距，并设计能突破吞吐量和可用性边界的优化方案。该职位结合了深入的系统理解与实际的性能工程——分析 GPU 内核性能、集体通信吞吐量，调查 I/O 瓶颈，并对模型进行分片以便在大规模上进行训练。
你将帮助确保我们的集群以最佳性能运行，使 OpenAI 能够在相同的计算预算下训练更大、更强大的模型。
该职位位于加利福尼亚州旧金山。我们采用每周三天在办公室的混合办公模式，并为新员工提供搬迁协助。
在此职位中，你将：
- 分析端到端训练运行，识别计算、通信和存储方面的性能瓶颈。
- 优化大规模分布式模型训练中的 GPU 利用率和吞吐量。
- 与运行时和系统工程师合作，提升内核效率、调度和集体通信性能。
- 实现模型图变换以提高端到端吞吐量。
- 构建工具来监控和可视化集群中的 MFU、吞吐量和可用性。
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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 https://cdn.openai.com/policies/eeo-policy-statement.pdf.
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.
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