Senior Machine Learning Engineer - 3D Segmentation

zoox

4100

Foster City, CA, USA

Posted on: 2026-02-05

Category: emobility

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Employment type:

Full time

Experience required:

Senior

Salary

Salary not provided

About the company:

Zoox is transforming mobility-as-a-service by developing a fully autonomous, purpose-built fleet designed for AI to drive and humans to enjoy.

The Perception team at Zoox is responsible for the robot’s understanding of the world, fusing data from Lidar, Radar, and Cameras to create a unified representation of the environment. In this role, you will contribute to the development of our next-generation 3D occupancy and segmentation networks. You will architect and optimize high-performance deep learning models that generate dense, temporally consistent voxel representations of the driving environment. This work is critical for enabling our vehicle to navigate complex urban scenarios, handle rare obstacles, and drive safely in tight spaces by providing precise geometry and motion estimates to downstream planners.

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.

Accommodations

If you need an accommodation to participate in the application or interview process please reach out to [email protected] 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.

In this role, you will:

Design and implement state-of-the-art multi-modal sensor fusion architectures (Lidar, Camera, Radar) to predict 3D occupancy, semantic segmentation, and flow .

Develop "vision-first" fusion strategies to enhance geometric understanding and reduce dependency on sparse sensor modalities .

Engineer temporal processing modules to improve the stability and consistency of predictions over time.

Optimize model architectures for real-time on-vehicle inference, balancing high-fidelity range extension with strict latency constraints .

Collaborate with downstream consumers (Tracking, Prediction, Planner) to refine geometric outputs, such as contours and free-space estimations, for complex maneuvering.

Qualifications

MS or PhD in Computer Science, Robotics, Machine Learning, or related field with 6+ years of industry experience.

Deep expertise in 3D Computer Vision and Deep Learning, specifically with voxel-based or BEV (Bird's Eye View) architectures.

Strong proficiency in Python and deep learning frameworks (PyTorch) for model training and design as well as some experience in C++ for model integration.

Experience with multi-sensor fusion (Lidar, Camera, Radar) and handling temporal data sequences.

Experience with occupancy networks, implicit representations (NeRF/Gaussian Splats), or scene flow estimation.

Bonus Qualifications

Experience optimizing models for TensorRT/CUDA to achieve low-latency inference.

Familiarity with sparse convolutions or query-based architectures for efficient 3D processing.

Experience with Vision Language Model or multi-modal 3D foundation model.

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