Technical Lead Manager - Training Runtime, Data(set) Movement
OpenAI
About the role
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, fast to restart, 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)