Senior Engineering Manager, ML Platform

Confirmed live in the last 24 hours

Zoox

Compensation

$317k - $370k/per-year-salary

Foster City, CA

On-site

Posted March 9, 2026

Job Description

Zoox is on a mission to reimagine transportation and build autonomous robotaxis from the ground up that are safe, reliable, clean, and enjoyable for everyone. With bidirectional driving capabilities and four-wheel steering, our vehicle allows us to maneuver through compact spaces and change directions without needing to reverse. We are still in the early stages of deploying our robotaxis, and it is a great time to join Zoox and have a significant impact on executing this mission.

Our growing Software Infrastructure engineering leadership team is looking for a Senior Engineering Manager, ML Platform. The centralized ML Platform team at Zoox plays a crucial role in enabling innovations across all our Autonomy and Data Science teams to develop and deploy models across our robotaxi and cloud infrastructure, and to work on cutting-edge training and inference optimization techniques.

The Opportunity

We are working on many interesting challenges to enable rapid experimentation and scale our multi-modal Foundation models and RL infrastructure, and ensure these models run efficiently on our vehicles, meeting our latency targets. You will get to work across all ML teams within Zoox - Perception, Prediction, Planner, Simulation, Collision Avoidance, and our Advanced Hardware Engineering group, and have the opportunity to significantly push the boundaries of how ML is practiced within Zoox.

We build and operate the base layer of ML tools, deep learning frameworks, and inference libraries used by our applied research teams for in- and off-vehicle ML use cases. You will lead a team of strong software engineers and managers and act as a force multiplier for our internal customers. This team has many growth opportunities as we expand our robotaxi deployments and venture into new ML domains. If you want to learn more about our ML Infrastructure, here is one of our past talks at re:Invent.

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