Senior Machine Learning Engineer
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.
About the job
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 Vision, Deep Learning, Voxel-Based Architectures, Bev Architectures, Python, PyTorch, C++, Multi-Sensor Fusion, Lidar, Camera, Radar, Temporal Data, Occupancy Networks, Nerf, Gaussian Splats
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