Senior Machine Learning Engineer - Perception 3D Segmentation

Zoox; Foster City (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Zoox is seeking a senior researcher/engineer to advance 3D occupancy and segmentation networks. You will design multi-modal fusion (Lidar, Camera, Radar) and build voxel-based or BEV models with temporal coherence for on-vehicle inference.

Collaboration with downstream teams will refine geometry outputs for complex urban driving scenarios. Requires 6+ years in 3D computer vision/ML, proficiency in PyTorch, and experience with TensorRT/CUDA and C++.

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 architectures.
• Strong proficiency in Python and deep learning frameworks (PyTorch) for model training and design; some C++ experience.

Responsibilities

• 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 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 for complex maneuvering.

Skills

3D computer vision

BEV architectures

Temporal data sequences

Real-time inference optimization

Education

MS or PhD in CS/Robotics/ML or related

Tools

PyTorch

C++

TensorRT/CUDA

Sparse convolutions

NeRF / Gaussian splats

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.

Responsibilities

• 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.
• 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.

Accommodations

If you need an accommodation to participate in the application or interview process please reach out to [e-mail] or your assigned recruiter.

Diversity and Inclusion

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.