You will…
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Lead and grow the ML Dataset Platform team as both its technical lead and people manager: set the technical direction, define the roadmap, hire, and mentor engineers.
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Define the APIs, tools and storage formats for Waabi's ML datasets, balancing maximal reuse of data and tooling against the specific needs of each training workload.
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Build the tooling to manage datasets at scale, including versioning and efficient incremental updates without full rebuilds.
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Design the APIs that let teams express training-time transformations along the continuum between fully reproducible ahead-of-time ETL and dynamic transformation within data loaders.
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Own the common dataloading tools used across workloads, including observability into bottlenecks and the throughput of training jobs.
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Streamline data access for multi-cloud training so that datasets are available as needed with predictable cost and latency.
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Make data lineage a first-class, fully automatic property of every dataset, from raw data through to the model trained on them.
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Partner with the ML Training Platform team on the boundary between data and training infrastructure, and with the ML development teams on transitioning shared-dataset tooling into the new platform.
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Drive adoption: work directly with model teams to migrate their training data onto the platform and feed what you learn back into your team’s roadmap.
Qualifications:
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Deep ML dataset experience: You have designed and built large-scale data infrastructure for ML training (storage formats, dataset versioning, ETL, dataloading) and understand the performance characteristics of petabyte-scale sensor or multimodal data.
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Strong software engineering and implementation: You have very strong Python skills and are comfortable in PyTorch training loops; you have strong software-engineering fundamentals and experience with distributed systems, cloud storage, and data processing frameworks (e.g., Spark, Ray, Beam).
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Proven technical leadership: You have led a platform or infrastructure effort used by multiple teams, defined its APIs, and driven adoption across an organization.
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People leadership: You have managed or tech-led a team of engineers, including hiring and mentoring, and can balance hands-on technical contribution with roadmap ownership and stakeholder management.
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Team player: You communicate clearly with researchers and engineers, build consensus across teams with different needs, and deliver in a fast-paced environment.
Bonus:
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Experience with ML data infrastructure in autonomous driving or robotics (multi-sensor logs, LiDAR, camera, HD maps).
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Experience with columnar and tensor storage formats (e.g., Parquet, Lance, WebDataset, Zarr) and dataset versioning tools.
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Experience with multi-cloud or hybrid training environments and the data-movement challenges that come with them.
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Experience with data lineage, metadata catalogs, or feature-store systems.