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As a Software Engineer on the release pipelines team, you will automate and optimize build systems, workflows, and package management for autonomous vehicle software.

As a Senior ML Engineer at Torc, you will develop and maintain ML frameworks, enabling advanced solutions for autonomous vehicles and improving ML platforms.

The software engineer will develop and maintain Linux software for autonomous vehicles, optimize performance, debug systems, and ensure system stability and security, collaborating with cross-functional teams.

Design and deploy object detection and tracking modules; develop ML algorithms; guide team members; improve data governance and quality assessment.

Annotate 2D images and 3D sensor (LiDAR) data to create 2D bounding boxes and 3D cuboids; review and correct pre-labeled data; accept/reject annotations; provide clear feedback; learn and use internal annotation tools; follow detailed guidelines and collaborate with the team to ensure high-quality training data for autonomous-vehicle AI models.

Label and review 2D images and 3D sensor (LiDAR) data using annotation tools to create bounding boxes, cuboids, and other shapes. Ensure annotations follow guidelines, correct or reject pre-labeled data, provide notes, learn internal tools, and collaborate to improve data quality for autonomous vehicle AI.

Provide functional safety systems engineering for compute platform components in autonomous trucks. Lead safety design discussions, derive safety requirements, perform HARA/FTA/FMEA/DFA analyses, maintain requirements in DOORS and Jama, and develop verification/validation strategies with cross-functional teams and suppliers.

The Senior ML Engineer will architect data pipelines, develop classification algorithms, manage data integration, and mentor team members in a cross-functional setting.

Lead design and delivery of cloud-based pipelines that convert multi-sensor AV logs into VLM/VLA training datasets. Build VLM-assisted auto-labeling, generate language-grounded reasoning labels, mine rare scenarios, define dataset schemas and quality metrics, and operationalize continuous dataset delivery. Mentor engineers and partner with model teams to improve downstream model performance using distributed compute and scalable MLOps.

Lead Requirements and Safety workstreams as a Technical Product Manager: prioritize needs, build cross-functional plans, define and verify acceptance criteria, manage dependencies, risks, and change, and report roadmap status to drive timely execution toward product milestones.