🕒 7 days ago
Train and ship computer vision and 3D perception models for ADAS, owning work across data, evaluation, and iteration to improve real-world driving performance under production constraints.
Senior Machine Learning Engineer - Perception 3D Segmentation
📅 Jul 17
3D Computer VisionDeep Learning3D Occupancy NetworksSemantic Segmentation
📜 Description
- Design multimodal sensor-fusion architectures to predict 3D occupancy, semantic segmentation, and scene flow.
- Build temporal processing modules that stabilize predictions over time.
- Optimize models for real-time on-vehicle inference under strict latency constraints.
- Collaborate with tracking, prediction, and planning teams to refine contours and free-space estimates for complex maneuvers.
🛠️ Requirements
- 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.
✨ Benefits
- Develop vision-first fusion strategies to improve geometric understanding and reduce reliance on sparse sensor modalities.
Full 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.
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.
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.
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