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.