Technical Lead Manager, ML Dataset Platform

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.

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.

Build the tooling to manage datasets at scale, including versioning and efficient incremental updates without full rebuilds.

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.

Own the common dataloading tools used across workloads, including observability into bottlenecks and the throughput of training jobs.

Streamline data access for multi-cloud training so that datasets are available as needed with predictable cost and latency.

Make data lineage a first-class, fully automatic property of every dataset, from raw data through to the model trained on them.

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.

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.

Experience with ML data infrastructure in autonomous driving or robotics (multi-sensor logs, LiDAR, camera, HD maps).

Experience with columnar and tensor storage formats (e.g., Parquet, Lance, WebDataset, Zarr) and dataset versioning tools.

Experience with multi-cloud or hybrid training environments and the data-movement challenges that come with them.

Experience with data lineage, metadata catalogs, or feature-store systems.