Senior, ML Engineer - 3D Reconstruction

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.

About 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.

Responsibilities

• 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.

Requirements

• 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.

Qualifications

• 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 Qualifications

• PPK/RTK (Post-Processed Kinematic / Real-Time Kinematic) GPS experience.
• GIS (Geographic Information Systems) experience.

Benefits

• A competitive compensation package that includes a bonus component and stock options.
• 100% paid medical, dental, and vision premiums for full-time employees.
• 401K plan with a 6% employer match.
• Flexibility in schedule and generous paid vacation (available immediately after start date).
• Company-wide holiday office closures.
• AD+D and Life Insurance.

Pay

Torc's total compensation package reflects the cost of labor across several geographic markets. Pay is based on job-related knowledge, skills, and experience and may vary depending on the specific role. The hiring range for this position is $177,300—$212,800 USD. The package also includes a corporate bonus, stock option plan, and may include sign-on payments, relocation, and other forms of compensation.