Senior Machine Learning Engineer Perception 3D Segmentation

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 (Birds 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.