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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. - Mentor engineers and scientists, setting best practices for model training, evaluation, and code quality. - Stay ahead of the research frontier by evaluating and adapting emerging techniques (e.g., BEV-based large models, vectorized map prediction, lane graph transformers) to production-grade perception. What You’ll Need to Succeed
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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. Bonus Points!
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Experience developing ML models for autonomous driving, mapping, or ADAS systems. - Familiarity with multi-modal fusion (camera, LiDAR, radar, HD maps). - Hands-on experience with BEV-based and topological prediction models. - Contributions to perception-related ML research (CVPR, NeurIPS, ICCV, ICLR, ICRA). - Strong intuition for data quality, bias mitigation, and uncertainty modeling. Work Location: For this position, we are open to hiring in either the Torc Montreal, Quebec (Canada) or Ann Arbor, MI (U.S.) office work locations in a hybrid capacity. We are also open to hiring Remote in the United States or Canada. Perks of Being a Full-time Torc’r
Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:
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A competitive compensation package that includes a bonus component and stock options
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100% paid medical, dental, and vision premiums for full-time employees
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401K plan with a 6% employer match
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Flexibility in schedule and generous paid vacation (available immediately after start date)
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AD+D and Life Insurance
