Torc Robotics

Machine Learning Engineer

• Develop and train machine learning models for learned behavior systems, including behavior cloning, imitation learning, and reinforcement learning
• Implement production-quality ML code for model training, evaluation, and inference within the autonomy stack
• Analyze model performance, identify failure modes, and propose improvements for robustness and generalization
• Contribute to model training pipelines and data workflows, curating behavior datasets from simulation, fleet logs, and on-vehicle data
• Collaborate with simulation, validation, and autonomy engineering teams to test learned behavior models across diverse driving environments
• Integrate learned behavior models into simulation and testing workflows
• Support tooling and infrastructure that improves experimentation speed, reproducibility, and model iteration
• Contribute to technical discussions regarding model architecture and training strategies
• Develop software for automated trucks and autonomous driving systems at Torc Robotics

Machine Learning Engineer II – Learned Planning, Reinforcement Learning

Engineer

• Review HD map geospatial data including lane geometry, road boundaries, intersections, signage, and lane connectivity against internal quality specifications and ground-truth sources
• Perform QC on map features derived from LiDAR point clouds, aerial/satellite imagery, and vehicle-collected sensor data
• Identify, document, and triage map defects, escalating safety-critical issues to mapping and autonomy engineering teams
• Define and refine QC metrics, sampling strategies, and audit workflows to measure and improve map accuracy at scale
• Partner with mapping vendors and internal teams to communicate quality standards and drive defect resolution
• Analyze recurring defect patterns and feed findings back into upstream map creation and annotation processes
• Maintain documentation of QC procedures, edge cases, and regional/route-specific map requirements
• Support readiness reviews for new lanes, corridors, or operational design domains before launch
• Collaborate with engineering teams to create tailored QC workflows and optimize scalable mapping processes

Quality Control Engineer, HD Mapping

General Counsel

• Own Torc's litigation docket end to end, including outside counsel, budgets, strategy, and resolution
• Build and run Torc's litigation readiness program, including legal hold procedures, response playbooks, and evidence preservation protocols
• Serve as legal lead on the cross-functional incident response team
• Develop litigation readiness frameworks for driver-out commercial operations, autonomous driving incidents, and recalls
• Manage Torc's outside litigation counsel panel
• Report litigation exposure and trends to the General Counsel and senior leadership
• Advise on record retention, insurance, and cybersecurity in relation to litigation readiness and incident response
• Partner with Product, Engineering, and Safety & Regulatory teams to identify and mitigate legal risk
• Advise on product liability exposure from Torc's autonomous driving system
• Support NHTSA crash, defect, recall, and Early Warning Reporting
• Provide legal analysis on product release decisions and safety artifact requirements
• Translate evolving AV regulations into practical internal guidance and assess litigation risk
• Advise on go-to-market issues for commercial freight operations
• Review external communications, technical publications, and customer-facing materials for legal risk
• Build expertise in Federal Motor Vehicle Safety Standards, ADS exemption processes, and FMCSA requirements
• Contribute to legal department process improvements, playbook development, and knowledge management

Senior Counsel – Litigation, Product

• Develop and deploy machine learning models for camera-based perception in autonomous trucks, including object detection, segmentation, depth estimation, and scene understanding
• Own end-to-end model development from data curation and training through evaluation and deployment
• Write production-quality ML code for scalable training, evaluation, and inference pipelines
• Analyze model performance across diverse driving scenarios, identify failure modes, and improve robustness and generalization
• Improve large-scale training pipelines, including dataset preparation, distributed training, and experiment tracking
• Partner with data teams on dataset quality, labeling strategies, and edge-case coverage
• Collaborate with perception, simulation, and validation teams to integrate models into the autonomy stack
• Improve tooling, workflows, and infrastructure for experimentation and model iteration
• Contribute to model architecture decisions and technical discussions
• Mentor junior engineers on implementation, debugging, and best practices

Senior Machine Learning Engineer – Camera Model

• Develop and deploy deep learning models for camera-based perception, including object detection, segmentation, depth estimation, and scene understanding
• Own end-to-end model development from data curation and training to evaluation and deployment
• Write production-quality ML code for scalable training, evaluation, and inference pipelines
• Analyze model performance across diverse driving scenarios, identify failure modes, and improve robustness and generalization
• Contribute to large-scale training pipelines, including dataset preparation, distributed training, and experiment tracking
• Partner with data teams to improve dataset quality, labeling strategies, and edge-case coverage
• Collaborate with perception, simulation, and validation teams to evaluate and integrate models into the autonomy stack
• Improve tooling, workflows, and infrastructure to accelerate experimentation and model iteration
• Contribute to model architecture decisions and technical discussions
• Mentor junior engineers on implementation, debugging, and best practices