[Hiring] Technical Lead Manager, ML Dataset Platform @Waabi

Role Description

As TLM of the ML Datasets Platform, you will play a critical role defining the APIs, tooling, and storage formats that make datasets a shared, first-class asset at Waabi:

• Versioned, efficiently updatable, fully traceable, and fast to load across GPU workloads.
• Find the right abstractions for ML teams to integrate with the platform without a one-size-fits-all mold.
• Act as a hands-on technical leader who grows and manages the team, sets its roadmap, and partners closely with the ML Training Platform team and ML development teams.

You will…

• 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.

Qualifications

• Deep ML dataset experience: 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.
• Strong software engineering and implementation: very strong Python skills, comfortable in PyTorch training loops, strong software-engineering fundamentals, and experience with distributed systems, cloud storage, and data processing frameworks (e.g., Spark, Ray, Beam).
• Proven technical leadership: led a platform or infrastructure effort used by multiple teams, defined its APIs, and driven adoption across an organization.
• People leadership: managed or tech-led a team of engineers, including hiring and mentoring, balancing hands-on technical contribution with roadmap ownership and stakeholder management.
• Team player: communicate clearly with researchers and engineers, build consensus across teams with different needs, and deliver in a fast-paced environment.

Requirements

• 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.

Benefits

• Competitive compensation and equity awards.
• Health and Wellness benefits encompassing Medical, Dental and Vision coverage (for full-time employees only).
• Unlimited Vacation.
• Flexible hours and Work from Home support.
• Daily drinks, snacks and catered meals (when in office).
• Regularly scheduled team building activities and social events both on-site, off-site & virtually.
• As we grow, this list continues to evolve!