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You&#39;ll build data systems behind the largest training runs on thousands of GPUs, where fixing one bottleneck lets researchers train the next breakthrough model.</p>\n<p><strong>What You&#39;ll Work On:</strong></p>\n<ul>\n<li>Scalable data loaders for training runs across thousands of GPUs</li>\n<li>Efficient storage and retrieval systems for petabyte-scale datasets</li>\n<li>Multi-cloud object storage abstraction</li>\n<li>Execute large-scale data migrations across storage systems and providers</li>\n<li>Debug and resolve performance bottlenecks in distributed data loading</li>\n</ul>\n<p><strong>Technical Focus:</strong></p>\n<ul>\n<li>Python, PyTorch DataLoader internals</li>\n<li>Object storage (e.g. S3, Azure Blob, GCS)</li>\n<li>Parquet for metadata</li>\n<li>Video: ffmpeg, PyAV, codec fundamentals</li>\n</ul>\n<p><strong>What We&#39;re Looking For:</strong></p>\n<ul>\n<li>Built and operated data pipelines at petabyte scale</li>\n<li>Optimized data loading</li>\n<li>Worked with petabyte-scale video and image datasets</li>\n<li>Written processing jobs operating on millions of files</li>\n<li>Debugged distributed system bottlenecks across large fleets of machines</li>\n</ul>\n<p><strong>Nice to Have:</strong></p>\n<ul>\n<li>Experience streaming dataset formats (e.g. WebDataset)</li>\n<li>Video codec internals and frame-accurate seeking</li>\n<li>Distributed systems experience</li>\n<li>Slurm and Kubernetes for job orchestration</li>\n<li>Experience with object storage performance tuning across providers</li>\n</ul>\n<p><strong>How We Work Together:</strong></p>\n<ul>\n<li>We&#39;re a distributed team with real offices that people actually use. 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