Back to jobs
Zoox1K–5K employees

Senior Manager, Perception Data

Foster City, CA$339,000 – $375,000Posted 3 days ago

Immigration summary

Visa sponsorship

LikelyLow confidence

This employer sponsors, but not for roles like this one.

321 recent H-1B filings

View visa evidence

Green card sponsorship

Strong historyMedium confidence

This employer has recently sponsored green cards at scale.

93 recent certified PERM filings

View green card evidence

Job description

Perception data is core to how Zoox’s robotaxis understand the world, and as Senior Manager of Perception Data, you will own the flywheel that turns that data into safer, smarter driving.

You will lead our perception data organization, spanning data science, data engineering, and data labeling, and own how we mine logs, automate annotation, feed high value data into our models, and close the loop through analysis and measurement. The most valuable examples in our corpus are often the hardest to find: rare behaviors, unusual interactions, safety critical events, and the long tail of real world urban driving. Your role is not only to build the systems that process this data, but to decide what data matters and why, and to build the learning loop that lets Zoox improve model performance faster.

This is an opportunity for a leader who blends strategic thinking with strong execution, has done it before at scale, and knows how to partner with ML leaders, metrics pipelines, and infrastructure teams.

In this role, you will...

  • Own the perception data flywheel. Run the learning loop from real world fleet signals and model behavior through data discovery, curation, and enrichment into training, evaluation, and measurement.
  • Lead multidisciplinary teams. Manage and develop a 10+ person team of data science, data engineering and data labeling, setting priorities and raising the bar on execution.
  • Automate annotation at scale. Drive auto annotation and auto labeling pipelines, reducing manual cost while improving label quality and throughput.
  • Build advanced data miners. Develop intelligent approaches to surfacing rare, surprising, and safety critical scenarios in very large datasets, using techniques such as embeddings, semantic search, learned representations, and model driven data selection.
  • Own log selection and storage. Define how we select, store, and retrieve fleet logs so the right data reaches our models efficiently and economically.
  • Connect data to model performance. Establish how we measure the value of data and make rigorous trade offs across quality, accuracy, speed, cost, and scale.
  • Close the loop. Feed curated data into model training and evaluation, then analyze outcomes to continuously refine what we collect and label.
  • Partner cross functionally. Work closely with ML, metrics and evaluation, and infrastructure teams to keep the flywheel running reliably at scale.
  • Qualifications

  • Track record of building and leading high performing technical teams (data science, data engineering, ML, or labeling), including managing managers or senior individual contributors.
  • Deep technical expertise in computer vision, video, multimodal AI, or related perception problems.
  • Experience leading large scale data capabilities that directly influence model training, evaluation, and performance.
  • Strong intuition for what makes data valuable, with experience in data discovery, selection, curation, and enrichment at scale.
  • Proven execution at scale, with strong technical and commercial judgment across quality, speed, cost, and build versus buy trade offs.
  • Ability to move between strategy and technical detail, set direction in ambiguity, and influence senior technical and business stakeholders. 
  • Bonus Qualifications

  • Autonomous driving, robotics, or embodied AI.
  • Large scale video, multimodal, or foundation model training.
  • Semantic search, embeddings, active learning, auto labeling, or other approaches to intelligent data selection and enrichment.
  • Large scale real world data acquisition across fleets, partners, or multiple geographies.
  • Sponsorship evidence

    Why Openbound reached the conclusions above.

    Visa sponsorship evidence

    Current posting

    Silent on sponsorship

    Employer H-1B history

    321
    recent certified H-1B filings
    188
    new-hire petitions
    0
    filings for similar roles
    347
    so far in FY2026
    More evidence details
    • 321 recent certified H-1B filings across the employer
    • Still filing this year — 347 filings in FY2026
    • 74 USCIS H-1B new-employment approvals, counted separately from LCA filings
    • Strong filing activity in CA
    • 334 further USCIS approvals for extensions or transfers
    • We checked 258 filing titles for this employer and none describe work like this role
    • The posting says nothing about sponsorship either way

    Strong filing activity in CA.

    Green card sponsorship evidence

    Employer PERM history

    93
    recent certified PERM filings
    0
    filings for similar roles
    92
    filings in this location
    Certified PERM filings by fiscal year
    202347
    202440
    2025143
    202674YTD

    Filing history reflects past employer behavior; it isn't a promise for this opening.

    All open roles at Zoox
    How Openbound evaluates sponsorship

    Visa history uses official U.S. Department of Labor H-1B LCA disclosure data and USCIS H-1B petition history. Green card history uses DOL PERM disclosure data. Each is read for the employer as a whole, for roles like this one, and for this location, weighted toward the most recent fiscal years.

    An employer is matched to its filing entities by verified legal name and reviewed aliases; a match is never made on a name resemblance alone. Where no verified entity can be matched, the page says so and draws no conclusion from the absence. 258 filing titles were examined for this employer.

    What this posting states outranks history in both directions, and an employer's published policy outranks past filings. Filing history reflects past behavior; it is not a promise of sponsorship for this opening, and none of this is legal advice.