Torc is looking to develop software for automated trucks to transform how the world moves freight, and this role is focused on leading the model development efforts for Road & Lane Detection to enable Torc’s autonomous vehicles to perceive and interpret road geometry, lane structures, and drivable surfaces with precision and robustness.
Requirements
- 10+ years of experience developing deep learning models for perception or computer vision at scale.
- M.S. or Ph.D. in Computer Science, Electrical Engineering, Robotics, or a related field (or equivalent experience).
- Deep expertise in semantic and instance segmentation, BEV modeling, or scene topology estimation.
- Strong understanding of lane and road geometry modeling, camera calibration, and sensor projection.
- Proficiency with Python and modern ML frameworks (e.g., PyTorch, Lightning).
- Experience with distributed training pipelines, experiment management, and large-scale dataset handling.
- Proven leadership in guiding technical roadmaps, mentoring engineers, and driving measurable model improvements.
Responsibilities
- Own the model roadmap for Road & Lane Detection within the Model Dev ML org — from concept through production-grade model maturity.
- Research, design, and train advanced neural architectures (e.g., multi-camera BEV transformers, LiDAR-vision fusion models, topological lane graph networks) to detect, segment, and model road structures and lane connectivity.
- Lead data strategy for this domain — defining data curation, labeling policies, and active learning pipelines to capture long-tail scenarios (e.g., occlusions, complex merges, construction zones).
- Develop robust metrics and evaluation frameworks for lane and road geometry accuracy, temporal consistency, and cross-domain generalization.
- Advance foundational capabilities such as self-supervised pretraining, synthetic-to-real adaptation, and temporal modeling for road and lane understanding.
- Drive large-scale experiments — designing, running, and analyzing results from distributed training workflows and ablations to identify scalable improvements.
- Collaborate with other model dev/perception teams to ensure model coherence and interface consistency.
Other
- M.S. or Ph.D. in Computer Science, Electrical Engineering, Robotics, or a related field (or equivalent experience).
- 10+ years of experience developing deep learning models for perception or computer vision at scale.
- Proven leadership in guiding technical roadmaps, mentoring engineers, and driving measurable model improvements.
- Ability to work in a collaborative and team-focused environment.
- Commitment to building a diverse and inclusive workplace.
