Develop next-generation 3D occupancy and segmentation networks for autonomous vehicles by fusing Lidar, Camera, and Radar data into temporally consistent voxel representations. Requires MS/PhD + 6+ years experience in 3D CV, multi-modal fusion, and PyTorch.
242k – 290k/yr
Hybrid6+ YOEML Engineering
About the role
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.
Requirements
MS or PhD in Computer Science, Robotics, Machine Learning, or related field.
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 PyTorch for model training and design.
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.
Nice-to-Haves
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, multi-modal 3D foundation model, World Model, or VLA.
Skills
3d computer visionDeep Learningvoxel-based architecturesbev architecturesPythonPyTorchC++multi-sensor fusionlidarcameraradartemporal dataoccupancy networksnerfgaussian splats
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