Zoox

Fully electric autonomous ride-hailing service platform

Staff / Senior Staff Machine Learning Engineer - Collision Avoidance System

Robotics & Autonomous SystemsUpdated on Nov 10

Full-timeFoster City, CA, USA$291k - $409k /yrHybrid

About the Job

Requirements

A BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field, or equivalent experience.
At least 10 years of experience developing perception or related algorithms for robotics, autonomous vehicles, or other real-time systems.
Deep expertise in 3D perception using lidar, radar, and/or camera, including hands-on machine learning methods such as detection, segmentation, and fusion, and classical methods such as geometry, state estimation, and tracking.
Strong C++ or Python skills and a track record of shipping efficient, reliable real-time software to production.
Experience leading the architecture and technical roadmap of a complex production system across multiple teams.

Responsibilities

Serve as the technical lead and architect for CAS perception, defining how lidar, radar, and camera data become reliable obstacle detection, tracking, and collision-risk estimates under tight latency and compute budgets.
Design, implement, and ship machine learning and classical perception algorithms, including point-cloud geometry, ground estimation, clustering, state estimation and tracking, learned detection and segmentation, and sensor fusion, in production-quality, safety-critical C++.
Own the end-to-end CAS algorithm architecture and multi-release technical roadmap, making trade-offs between safety, performance, latency, compute, and machine learning versus non-machine-learning approaches.
Partner across CAS perception, behavior and planning, and validation to define interfaces, requirements, metrics, and evaluation strategies that show the system is safe and ready to launch.
Work with sensor, system performance, safety strategy, and main autonomy teams to drive cross-team technical decisions, while mentoring engineers and raising the technical bar across CAS.

Desired Qualifications

Conference or journal publications in Machine Learning or Robotics-related venues.
Experience with safety-critical systems or functional safety practices such as ISO 26262 and SOTIF, collision avoidance, emergency braking, prediction, or planning systems for autonomous vehicles.
Experience optimizing and deploying models or algorithms on onboard compute, such as GPU/CUDA and TensorRT.

Salary Range: 291000 to 409000 (Currency: USD) (Pay period: per-year-salary)

The Collision Avoidance System (CAS) is Zoox's independent safety layer. It runs alongside our main autonomy stack, constantly watches for imminent collisions, and reacts within milliseconds to prevent or reduce them. By using different methods and data paths than the main stack, CAS gives our robotaxi a second, diverse line of defense in the most challenging dense urban environments.

As a Staff or Senior Staff engineer, you will be the technical lead for CAS algorithms, with a focus on CAS perception. You will shape how CAS sees the world, combining interpretable geometric methods with machine learning, and you will own the architecture and roadmap that take these capabilities from idea to safe, real-time deployment on our fleet. Your work will directly affect the safety of every rider and road user around our vehicles.

In this role, you will:

Serve as the technical lead and architect for CAS perception, defining how lidar, radar, and camera data become reliable obstacle detection, tracking, and collision-risk estimates under tight latency and compute budgets
Design, implement, and ship both machine learning and classical perception algorithms (e.g., point-cloud geometry, ground estimation, clustering, state estimation and tracking, learned detection and segmentation, sensor fusion) in production-quality, safety-critical C++
Own the end-to-end CAS algorithm architecture and multi-release technical roadmap, making clear trade-offs between safety, performance, latency, compute, and ML vs. non-ML approaches
Partner across CAS perception, behavior and planning, and validation to define interfaces, requirements, metrics, and evaluation strategies that show the system is safe and ready to launch
Work with sensor, system performance, safety strategy, and main autonomy teams to drive cross-team technical decisions, while mentoring engineers and raising the technical bar across CAS

Qualifications

BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field, or equivalent experience
At least 10 years of experience developing perception or related algorithms for robotics, autonomous vehicles, or other real-time systems
Deep expertise in 3D perception from lidar, radar, and/or camera, with hands-on experience in both machine learning (detection, segmentation, fusion) and classical methods (geometry, state estimation, tracking)
Strong C++ or Python skills and a track record of shipping efficient, reliable real-time software to production
Experience leading the architecture and technical roadmap of a complex production system across multiple teams

Bonus Qualifications

Conference or Journal publications in Machine Learning or Robotics-related venues
Experience with safety-critical systems or functional safety practices (e.g., ISO 26262, SOTIF), collision avoidance, emergency braking, prediction, or planning systems for autonomous vehicles
Experience optimizing and deploying models or algorithms on onboard compute (e.g., GPU / CUDA, TensorRT)

About the Company

Zoox

Fully electric autonomous ride-hailing service platform

Zoox designs and operates an autonomous ride-hailing service built around a purpose-built, fully electric vehicle designed specifically for autonomous operation. The vehicle uses advanced sensors, cameras, and AI to navigate complex urban environments, offering a four-passenger interior for comfort. Unlike competitors that retrofit existing cars, Zoox engineers both the vehicle and the service from the ground up, enabling optimized safety, efficiency, and passenger experience through integrated software that optimizes routing and reduces wait times. The company emphasizes safety through extensive testing and regulatory collaboration, and sustainability via electric mobility. The ultimate goal is to provide a smarter, safer, and more efficient urban transportation system that improves city life while reducing emissions.

Company size: 5,001-10,000
Company stage: Acquired
Total funding: $1.2B
Headquarters: Menlo Park, CA
Founded: 2014

Recently Posted Jobs

Explore more opportunities at Zoox

See more jobs