技术负责人经理 - 培训运行时，数据迁移
Technical Lead Manager - Training Runtime, Data(set) Movement
关于团队
Training Runtime 负责构建支持 OpenAI 最大模型训练运行的分布式系统——最近的 GPT-5.5 就是其中的代表！Data Movement 团队负责维护基础设施，确保训练任务在正确的时间获得正确的数据，并且在大型集群中安全高效地移动模型状态。
我们的工作涵盖机器学习系统、分布式存储、高吞吐数据加载、可靠性工程和开发者体验。成功意味着研究人员可以快速推进，同时训练任务在大规模下依然保持快速、可复现、可调试和具备弹性。
关于职位
我们正在寻找一位深入技术的 Technical Lead Manager，负责管理训练基础设施中的数据集。该职位将决定训练任务如何读取数据：包括 API、存储协议、版本控制模型、基准测试、调试工具和可靠性保证，这些都将使数据访问在当前和未来的训练框架中保持一致。
你将作为数据集读取的主要技术负责人开始工作，直接参与代码开发，同时协调研究人员、训练框架负责人、存储团队和基础设施合作伙伴，围绕一个持久的平台进行协作。在前沿规模下，这个问题看似简单但实际非常复杂：让庞大的、异构的数据集易于使用，在分布式工作者之间保持正确性，在出现问题时可观察，并且足够灵活以支持预训练、强化学习和多模态训练。
在这个职位中，你将：
- 设计并构建适用于多个当前和未来训练框架的统一数据集读取平台。
- 定义数据集 API、存储格式预期、注册/版本控制以及迁移路径，使数据访问具有可复现性和可维护性。
- 在读取路径中引入可靠性，包括有状态迭代、缓存、快速重启、恢复和清晰的操作协议。
- 构建终端和基于网络的可视化工具，让团队能够在管道后期检查文本、多模态和强化学习数据，此时错误最容易被发现。
- 编写和审查核心数据加载、服务、缓存和可靠性路径中的生产代码。
- 与从事训练框架、强化学习、多模态模型、存储、运行时和集群基础设施的团队合作。
长期来看
长期目标是建立一个团队，负责训练中的快速、准确、可扩展和可靠的集群内数据传输：进入集群的数据、离开集群的数据以及集群内部的数据。
查看英文原文
ABOUT THE TEAM
Training Runtime builds the distributed systems that power OpenAI's largest model training runs - most recently GPT-5.5! The Data Movement area owns the infrastructure that keeps training jobs supplied with the right data at the right time, and keeps model state moving safely and efficiently across large clusters.
Our work spans machine learning systems, distributed storage, high-throughput data loading, reliability engineering, and developer experience. Success means researchers can move quickly while training runs remain fast, reproducible, debuggable, and resilient at scale.
ABOUT THE ROLE
We are looking for a deeply hands-on Technical Lead Manager to own datasets throughout our training infrastructure. This person will set the direction for how training jobs read data: the APIs, storage contracts, versioning model, benchmarks, debugging tools, and reliability guarantees that make data access consistent across current and future training frameworks.
You will begin as the primary technical owner for dataset reads, working directly in the code while aligning researchers, training framework owners, storage teams, and infrastructure partners around a durable platform. The problem is deceptively hard at frontier scale: make enormous, heterogeneous datasets easy to consume, correct across distributed workers, observable when something goes wrong, and flexible enough to support pretraining, reinforcement learning, and multimodal training.
IN THIS ROLE, YOU WILL
- Design and build a unified dataset read platform for multiple current and future training frameworks.
- Define dataset APIs, storage-format expectations, registration/versioning, and migration paths that make data access reproducible and maintainable.
- Build reliability into the read path, including stateful iteration, caching, fast restart, recovery, and clear operational contracts.
- Build terminal and web-based visualizers that let teams inspect text, multimodal, and reinforcement learning data late in the pipeline, where bugs are most visible.
- Write and review production code in core data loading, service, caching, and reliability paths.
- Partner with teams working on training frameworks, reinforcement learning, multimodal models, storage, runtime, and cluster infrastructure.
OVER TIME
The long-term goal is a team that owns fast, correct, scalable, and reliable in-cluster data movement for training: data that comes in, data that goes out, and data that moves around inside the cluster. After ramping on datasets, this role will expand to TLM ownership for broader data movement systems, including checkpoint loads/saves and snapshot transfers, while partnering closely with existing technical leads and adjacent infrastructure teams.
YOU MIGHT THRIVE IN THIS ROLE IF YOU:
- Have built or owned dataset, data loading, storage, or distributed training infrastructure at large scale (e.g. torch.utils.data http://torch.utils.data)
- Care equally about API design, debugging ergonomics, performance, and bit-level correctness.
- Understand the failure modes of large distributed training jobs and know how data systems can create or prevent them.
- Have experience with stateful iterators, checkpoint/restart semantics, caching, remote services, or high-throughput storage reads.
- Are comfortable working across Python and lower-level systems code; Rust or C++ experience is useful but not required.
- Have worked with multimodal, video, reinforcement learning, or pretraining data pipelines where small data bugs are expensive and hard to diagnose.
- Can lead through code and technical judgment before a team exists, and can later manage engineers without losing the hands-on edge.
- Obsess over developer experience by eliminating friction, such as manual preprocessing scripts and niche cluster-specific bugs, ensuring a reliable and efficient experience for researchers.
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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