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.

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.