Senior, ML Engineer - 3D Reconstruction

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. What You’ll Do:

Design, implement, test and deploy offline 3D reconstruction, lane line detection, and automatic mapping/map creation modules to generate high-quality annotations on Cloud Services from logged sensor data (Cameras, Lidars, Radars, GPS/IMU). Build and refine lane line annotation pipelines, applying the latest lane line detection and creation machine learning models to automate and scale map creation. Develop and improve pose estimation algorithms to support accurate localization, sensor fusion, and 3D scene reconstruction. Demonstrate project management skills, serving as project lead guiding less experienced team members in multiple facets of project execution. Stay up to date with the latest developments in AI and ML for autonomous driving, 3D reconstruction, and automated mapping. 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. 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 or business process expertise, technical leadership, coaching and mentoring to team members.

What You’ll Need to Succeed:

Considered highly skilled and proficient in discipline; conducts complex, important work under minimal supervision and with wide latitude for independent judgment. Scope of Influence: Expected to drive alignment across team interfaces to the rest of the organization. Designs, maintains and owns team technical solutions and drives consensus. Mentors and guides engineers within the group. Bachelor’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; Master’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 3+ years of experience OR; Required Qualifications (some combination of the following skills):

Experience in lane line annotation creation or automatic mapping/map creation. Familiarity with the latest lane line detection and creation machine learning models. Familiarity with pose estimation. Active Learning & Pseudo-labeling – Computer Vision, Deep Learning, Model training. Two of the following: 2D/3D Object Detection, Tracking, Sensor Fusion, Semantic Segmentation, SLAM, BEV. Scaled ML Operations (MLOps) and Tooling – ML Frameworks, experiment tracking, model registry, MLflow, Weights and Biases, ML Metrics and Evaluation / Quality. Distributed machine learning frameworks – PyTorch, Lightning, Ray. Model Data Curation – Parquet data processing (PyArrow, Daft, Pandas, etc). Development Tools & Eco-System (at scale) – Proficiency in Python software development. Also, VDI and cloud-based development environments, CI Systems (GitHub Actions), and Docker.

Bonus Points!

PPK/RTK (Post-Pro