Senior, ML Engineer - 3D Reconstruction

About this role

About the Company

At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight. Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.

Meet The Team:

The Pseudo-Labeling team's goal is to create high-quality annotations on sensor data (images, point clouds). The annotations include 2D, 3D bounding boxes, classes, trajectories, lane lines, segmentations, depths, and high-definition map elements. The annotations are then used by different downstream users — for example, perception teams use them to train various models, mapping teams use them to build and maintain HD maps, and simulation teams use them for generating new data. This role owns the hardest problems in the team's lane line and mapping stack and is expected to set technical direction across lane line reconstruction, map creation, and pose accuracy.

What You’ll Do:

• Own the design, implementation, and deployment of offline 3D reconstruction, lane line detection, and automatic mapping/map creation systems that generate high-quality annotations on Cloud Services from logged sensor data (Cameras, Lidars, Radars, GPS/IMU).

• Lead lane line network reconstruction efforts, rebuilding topologically correct lane line graphs from noisy, partial, or conflicting detections across drives.

• Design, train, and iterate on lane line detection and creation models, pushing state-of-the-art performance and driving model architecture and training strategy decisions for the team.

• Develop methods for creating lane line maps in previously unmapped areas, and for detecting and propagating map updates as road geometry changes over time.

• Own pose correction for sensor data, and lead the diagnosis and mitigation of calibration and sensor errors (extrinsic/intrinsic miscalibration, drift, noise) that degrade reconstruction and mapping quality.

• Set technical direction and best practices for the team's lane line, mapping, and pose estimation pipelines, and lead architecture decisions with cross-team impact.

• Serve as technical lead on high-complexity projects, guiding and mentoring other engineers across multiple facets of project execution.

• Stay ahead of the latest developments in AI and ML for autonomous driving, 3D reconstruction, and automated mapping, and bring emerging techniques into production.

• Independently develop offline perception and mapping models or algorithms using disciplined software development processes, making recommendations for developing new code or re-using existing code, implementing version control, and maintaining documentation of created applications.

• Define and implement ingestion, data preparation, curation, and governance of large, multi-faceted data sets supporting analytics models and workflows.

• Proactively assess current capabilities to identify areas for improvement, proposing solutions that align with core strategy and operation.

• Measure and track auto-labeling and map creation quality to meet internal customer requirements, including quantifying the impact of pose and calibration error on downstream quality.

• Guide and produce information products, supporting visualization and data accessibility in a customer-centric manner.

• Evaluate and make recommendations regarding technical advances that improve productivity and quality, reduce flow times, and enhance operational surety.

• Develop guidelines and standards for analytics and machine learning models, their deployment, and associated processes.

• Provide technical guidance, technical leadership, coaching and mentoring to team members.

What You’ll Need to Succeed:

• Considered an expert and highly skilled in discipline; conducts highly complex, important work independently with minimal to no supervision and broad latitude for independent judgment.

• Scope of Influence: Expected to drive alignment across team and organizational boundaries. Sets technical direction, owns team technical solutions end-to-end, and is looked to as the authority for lane line, mapping, and pose estimation problems.

• Mentors and guides engineers within and beyond the group.

• Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 8+ years of experience OR;

• Master’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 6+ years of experience OR;

• PhD in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 3+ years of experience.

• Required Qualifications (some combination of the following skills):

• Strong, demonstrated background in lane line detection and creation, including hands-on experience with lane line network reconstruction and training lane line creation models.

• Proven experience creating lane line maps for previously unmapped areas, and handling ongoing map updates as road conditions and geometry change.

• Strong background in pose correction, with direct experience diagnosing and correcting calibration and sensor errors across camera, lidar, radar, and GPS/IMU systems.

• Active Learning & Pseudo-labeling – Computer Vision, Deep Learning, Model training at an advanced level.

• Two or more of the following at depth: 2D/3D Object Detection, Tracking, Sensor Fusion, Semantic Segmentation, SLAM, BEV.

• Scaled

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