Senior Machine Learning Engineer – Perception 3D Segmentation

Zoox; Foster City (CA); On-site; USD 254,000 - 350,000; Full time

Job summary

Zoox is seeking a senior role to advance 3D occupancy, segmentation, and multi-modal fusion for autonomous driving in Foster City, CA. You will architect high-performance deep learning models and ensure real-time inference on vehicle hardware.

The role emphasizes voxel-based/BEV architectures, temporal data handling, and collaboration with downstream teams to refine geometry outputs for safe maneuvering.

Qualifications

• MS or PhD in CS/Robotics/ML with 6+ years of industry experience.
• 3D Computer Vision and Deep Learning expertise, voxel/BEV architectures.
• Proficiency in Python and PyTorch; some C++ experience.
• Experience with multi-sensor fusion (Lidar, Camera, Radar) and temporal data.
• Experience with occupancy networks or implicit representations.

Responsibilities

• Design and implement multi-modal sensor fusion architectures to predict 3D occupancy and segmentation.
• Develop vision-first fusion strategies to enhance geometric understanding.
• Engineer temporal processing modules to improve stability over time.
• Optimize models for real-time on-vehicle inference with latency constraints.
• Collaborate with downstream teams to refine geometry outputs for complex maneuvering.

Skills

3D Computer Vision

Deep Learning

Voxel BEV architectures

Python

PyTorch

C++

Sensor Fusion

Temporal Data

Occupancy Networks

Implicit Representations

Education

MS or PhD in CS/Robotics/ML

Tools

TensorRT/CUDA

Sparse convolutions

Vision-Language Models

Job description

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.

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, or World Model, or VLA.

Base Salary Range

$254,000 – $350,000 a year

Base Salary Range There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign‑on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate’s relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.

Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long‑term care insurance, long‑term and short‑term disability insurance, and life insurance.

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 [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. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.