Technical Lead Manager, ML Dataset Platform

Waabi

. Lead and grow the ML Dataset Platform team as its technical lead and people manager .

Posted 11/5/2026full-timeUnited StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading ML dataset platform development, including defining APIs, managing large-scale data infrastructure, and mentoring engineering teams. Proficient in Python and familiar with distributed systems and cloud storage solutions for efficient data processing.

Highest-signal resume keywords

ML Dataset ExperienceTechnical LeadershipPython ProgrammingAPI DefinitionDistributed Systems

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

ML Dataset InfrastructureDataset VersioningETLData LoadingPerformance OptimizationData LineageMetadata CatalogsFeature Store SystemsColumnar Storage FormatsTensor Storage Formats

Soft Skills

Clear CommunicationConsensus BuildingStakeholder Management

Tools & Technologies

PyTorchSparkRayBeamMulti-Cloud Environments

Industry Keywords

Autonomous DrivingRobotics

Tech Stack

Tools & technologies

CloudDistributed SystemsETLPythonPyTorchRaySpark

About the role

Key responsibilities & impact

Lead and grow the ML Dataset Platform team as its technical lead and people manager
Set the technical direction and define the roadmap
Hire and mentor engineers
Define APIs, tools, and storage formats for Waabi's ML datasets
Build tooling to manage datasets at scale, including versioning and efficient incremental updates without full rebuilds
Design APIs for training-time transformations between reproducible ahead-of-time ETL and dynamic data-loader transformations
Own common dataloading tools across workloads, including observability into bottlenecks and training-job throughput
Streamline data access for multi-cloud training with predictable cost and latency
Make data lineage fully automatic from raw data through the trained model
Partner with the ML Training Platform team on data and training infrastructure boundaries
Partner with ML development teams to transition shared-dataset tooling into the new platform
Drive adoption by migrating model teams' training data onto the platform and incorporating feedback into the roadmap

Requirements

What you’ll need

Deep ML dataset experience: designed and built large-scale data infrastructure for ML training, including storage formats, dataset versioning, ETL, and dataloading
Understanding of the performance characteristics of petabyte-scale sensor or multimodal data
Very strong Python skills
Comfortable in PyTorch training loops
Strong software-engineering fundamentals
Experience with distributed systems, cloud storage, and data processing frameworks such as Spark, Ray, or Beam
Proven technical leadership of a platform or infrastructure effort used by multiple teams
Experience defining APIs and driving adoption across an organization
Experience managing or tech-leading a team of engineers, including hiring and mentoring
Ability to balance hands-on technical contribution with roadmap ownership and stakeholder management
Clear communication with researchers and engineers
Ability to build consensus across teams with different needs and deliver in a fast-paced environment
Experience with ML data infrastructure in autonomous driving or robotics is a bonus
Experience with columnar and tensor storage formats such as Parquet, Lance, WebDataset, or Zarr and dataset versioning tools is a bonus
Experience with multi-cloud or hybrid training environments and related data-movement challenges is a bonus
Experience with data lineage, metadata catalogs, or feature-store systems is a bonus

Benefits

Comp & perks

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