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.
Skills Required
- MS or PhD in Computer Science, Robotics, Machine Learning, or related field with 6+ years industry experience
- Deep expertise in 3D Computer Vision and Deep Learning (voxel-based or BEV architectures)
- Proficiency in Python and deep learning frameworks (PyTorch)
- Experience in C++ for model integration
- Experience with multi-sensor fusion (Lidar, Camera, Radar) and temporal data sequences
- Experience with occupancy networks, implicit representations (NeRF/Gaussian Splats), or scene flow estimation
- Experience optimizing models for TensorRT/CUDA for low-latency inference
- Familiarity with sparse convolutions or query-based architectures for efficient 3D processing
- Experience with Vision Language Models or multi-modal 3D foundation/world models
Zoox Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Zoox and has not been reviewed or approved by Zoox.
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Healthcare Strength — Healthcare is extensive, with broad medical and vision options, company‑paid disability coverage, and multiple mental‑health resources. Feedback suggests coverage breadth and auxiliary programs support a wide range of needs.
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Parental & Family Support — Family supports include paid parental leave, additional pregnancy disability time, fertility coverage, and adoption/surrogacy assistance. Backup care and family‑oriented programs further reinforce support across life stages.
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Wellbeing & Lifestyle Benefits — Day‑to‑day perks are robust, including free daily meals, fitness subsidies, commuter support, and on‑site amenities. Feedback suggests these lifestyle benefits enhance convenience and workplace experience, especially for office‑based roles.
Zoox Insights
What We Do
Zoox is an autonomous mobility company that was founded to provide a safer, cleaner, and more enjoyable future on the road. To achieve that goal, the company has spent the past 10 years creating a purpose-built robotaxi that gives the world a better way to ride.
Why Work With Us
At Zoox, we are working to solve one of the greatest technological challenges of our generation. From the beginning, we have been focused on our goal of reimagining transportation from the ground up. We are a mission-driven community of innovators working together to create a safer, cleaner, and more enjoyable future on the road.
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