5+ years in product or engineering roles focusing on data infrastructure in AV, Automotive, Robotics, or Edge AI sectors.
Proven experience in building production-level ML Ops pipelines.
Expertise in training datasets and evaluation for AI models.
Preferred experience with edge and depot data infrastructure (e.g., NAS) and cloud systems.
Familiarity with ML Ops practices, including targeted and active learning techniques.
Responsibilities
Develop and lead the roadmap for the Autonomy Data Flywheel initiative.
Create requirements and ensure the delivery of data needs for AI, Perception, and Safety teams.
Collaborate with engineering teams to set and meet quality and quantity KPIs.
Establish and oversee Data Diversity metrics to enhance scenario coverage for Sidewalk ODD.
Coordinate cross-functional projects with Operations and IT to improve depot and cloud infrastructure.
Maintain data pipelines to ensure proper data retention without compromising safety and daily operations.
Benefits
Flexible work arrangements, including remote opportunities across the U.S. and Canada.
Collaborative and respectful work environment with a diverse team.
Exposure to cutting-edge technology in robotics and AI.
Engagement in projects solving real-world urban delivery challenges.
Full Job Description
Data is an integral part of Serve's autonomy stack. As of EOY 2025, Serve has 2000 actively deployed robots making real world deliveries across numerous US cities. 'Autonomy Data Flywheel' is an internal Data product initiative that leverages on this large scale of real world robots and super-charges our End-End AI model development. It covers the entire data lifecycle starting with the data offloading infrastructure on edge, depots, and meeting the engineering needs of training, testing & validation.
Responsibilities
Build and drive roadmap for the Autonomy Data Flywheel
Draft requirements and ensure delivery for all consumers of the data including End-End AI, Perception, Safety and other teams.
Work with engineering teams to set and achieve targets for both quality and quantity KPIs
Define and maintain Data Diversity metrics for maximizing scenario coverage on Sidewalk ODD(Operational Design Domain)
Cross functional initiatives with Operations and IT teams to drive depot and cloud infrastructure work.
Support Data pipelines so that we are retaining the right data without impacting safety and day to day operations
Qualifications
Product/Engineering Experience for data infrastructure in AV/Automotive or Robotics/Edge AI industries
Experience building production level ML Ops pipelines
Fluent with training datasets and evals for AI models
Preferred: Experience with data infrastructure at Edge/Depot (E.g. NAS infra) and Cloud
Experience with ML Ops, targeted and active learning approaches
*Please note: The listed base salary range applies to candidates based in the San Francisco Bay Area. Compensation may vary depending on location, experience, and role alignment. We are open to qualified candidates working remotely within the United States and Canada
