Staff ML Engineer – Road & Lane Detection

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Torc Robotics

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Transport • Hardware • Artificial Intelligence

Torc Robotics is an innovative company focused on commercializing self-driving trucks for long-haul freight transportation. As an independent subsidiary of Daimler Truck, the company is developing autonomous technology, primarily focusing on the Freightliner Cascadia. Torc is committed to safe transportation, continuously improving its solutions through rigorous testing and integration of industry-leading sensors. It collaborates with fleet management companies to deploy real-world autonomous solutions, aiming to lead the industry in autonomous trucking.

501 - 1000 employees

Founded 2007

🚗 Transport

🔧 Hardware

🤖 Artificial Intelligence

📋 Description

• 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.

🎯 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.

🏖️ Benefits

• A competitive compensation package that includes a bonus component and stock options • Medical, dental, and vision for full-time employees • RRSP plan with a 4% employer match • Public Transit Subsidy (Montreal area only) • Flexibility in schedule and generous paid vacation • Company-wide holiday office closures • Life Insurance