Torc Robotics - ML Engineer, II - Birds Eye View (BEV)

• Develop and train machine learning models for scene understanding, including tasks such as object detection, road and lane prediction, semantic voxel grid classification, occupancy prediction, and map understanding in bird’s-eye-view (BEV) space. • Implement production-quality ML code to support model training, evaluation, and inference within the perception stack. • Analyze model performance, identify failure modes, and propose improvements to increase robustness across diverse driving environments and conditions. • Identify and interpret objects, lanes, obstacles, and weather conditions in the driving environment. • Contribute to multi-modal perception systems, combining signals from LiDAR, cameras, radar, and map sources into unified scene representations. • Work with large-scale datasets from simulation, fleet logs, and on-vehicle data to curate training data and improve model performance. • Collaborate with data, deployment, and infrastructure teams to evaluate perception models and ensure reliable performance in real-world driving scenarios. • Help integrate perception models into the autonomy stack and testing pipelines, enabling faster experimentation and iteration. • Contribute to tooling and infrastructure that improves training efficiency, experiment tracking, and reproducibility. • Participate in technical discussions around model architectures, sensor fusion strategies, and training approaches within the team.