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Zoox

Data Scientist, Fleet Orchestration Algorithms

Compensation
$177K–$217K / yr
Location
Foster City, CA, US
Arrangement
On-site
Employment type
Full-time
Posted
21 October 2026 (25 days ago)

Checked 2 days agoApplications go to the employer, never to RoleSprint

About this role

The Core Data Science team builds the products and data assets that let teams across Zoox measure and improve the autonomy and operational performance of our fleet. In this role, you'll focus on fleet simulation — the environment our data teams use to develop, test, and evaluate the algorithms that orchestrate our autonomous fleet (dispatch, routing, and vehicle assignment) and to understand how those algorithms perform before they reach the road.

You’ll piece together the constituent components into a coherent, easy-to-use simulation environment, filling in the gaps, and building generalizable mechanisms so data scientists can measure their algorithm's impact on fleet efficiency. You'll join a diverse, experienced team with rapidly growing scope while working with one of the industry's most valuable autonomous vehicle datasets.

In this role, you will: • Design, prototype, and help deploy algorithms for vehicle assignment, staging, routing, and pricing

• Model the inputs to orchestration (rider demand, data-collection value, servicing needs, events, and policies) as objectives and constraints

• Work with specialists in marketplace economics, forecasting, and autonomy to turn their models into decisions the fleet acts on

• Measure impact on revenue, utilization, and rider experience through simulation, offline evaluation, and online experiments

• Contribute to how we evaluate and roll out orchestration improvements

Qualifications • PhD or MS with 2+ years of relevant industry or research experience, in operations research, robotics, controls, transportation, computer science, applied math, or a related field

• Deep expertise in at least one of: combinatorial/integer optimization, stochastic optimization or control, reinforcement learning/approximate DP, network flows, or marketplace/demand modeling, plus the curiosity to work across the rest

• Strong coding skills in Python, C++, or Rust; you write clean, tested, version-controlled code

• Ability to turn ambiguous real-world problems into well-defined models and explain the tradeoffs to non-specialists

• Good communication and collaboration skills

Bonus Qualifications • Internship or research experience in ride-hailing, delivery, logistics, or AV fleets (dispatch, routing, rebalancing, pricing)

• Algorithms deployed in production or used on real operational data

• Publications in operations research, transportation, or robotics venues

About Zoox Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.

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Accommodations If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter.

A Final Note: You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

Work location

  • Foster City, CA, US

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About this listing

Advertised by Zoox and published on Lever, the applicant tracking system they use.

RoleSprint is not the employer and not a recruiter. Applications are made on the employer’s own site and never reach us; what RoleSprint does is help you decide whether a role is worth your time and prepare for it if it is.

Published 21 October 2026, last checked 2 days ago. A posting stops being advertised here 90 days after the employer published it, and one the employer takes down is marked closed rather than quietly removed.

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