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 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.
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.
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