Senior, ML Engineer - Offline Perception

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 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.

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 more. The annotations are used by downstream teams – perception teams train models with them, and simulation teams generate new data.

• Design, implement, test and deploy offline object detection, tracking and fusion modules to automatically create annotations on Cloud Services from logged sensor data (Cameras, Lidars, Radars).
• Serve 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.
• Independently develop offline perception models or algorithms, following disciplined software development processes, version control, and maintenance.
• Document 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 and propose solutions aligned with core strategy.
• Measure and track auto-labeling quality to meet internal customer requirements.
• Guide and produce information products, supporting visualization and data accessibility in a customer‑centric manner.
• Evaluate and recommend 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, business process expertise, leadership, coaching, and mentoring to team members.

• Highly skilled and proficient, conducting complex work under minimal supervision with wide latitude for independent judgment.
• Scope of influence:
  Expected to drive alignment across team interfaces to the rest of the organization, design, maintain, and own team technical solutions and drive consensus. Mentors and guides engineers within the group.
• Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus 6+ years of experience, OR Master’s Degree in the same fields plus 3+ years of experience.
• Required Qualifications (some combination of the following skills):
  • 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 & 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 and ecosystem (at scale) – Proficiency in Python software development, VDI and cloud-based development environments, CI systems (Git Hub Actions), and Docker.

• Data operations and management at scale – Schema design, AWS storage and processing infra, vector databases / Lance
  
  DB, file formats (MCAP, parquet, etc.).
• Data visualization – Integration with tooling such as OpenGL, 3.js, foxglove, Tableau.
• Cloud development – Python (proficient), Terraform, AWS Managed Services (S3, ECS, Lambda, Dynamo, Step Functions, Athena).
• Cloud-based orchestration and resource management – AWS Hyperpods, Anyscale, etc. Model inference orchestration.