Senior, ML Engineer - Offline Perception

About the Role

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. 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 etc. The annotations are then used by different downstream users, for example, perception teams use it to train various models and simulation teams use it for generating new data.

Responsibilities

• 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)
• Demonstrated 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.
• Independently develop offline perception 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 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.
• Provides technical guidance or business process expertise, technical leadership, coaching and mentoring to team members.

Requirements

• Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences 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 demonstrates competences and technical proficiencies typically acquired through 3+ years of experience OR;
• 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 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.

Qualifications

• Data operations and management at scale - Schema design, AWS storage and processing infra, vector databases / LanceDB, file formats (MCAP, parquet, etc).
• Data Visualization - Integration with tooling such as OpenGL, 3.js, foxglove, 51, tableau.
• Cloud Development – Python (proficient level), Terraform, AWS Managed Services (eg S3, ECS, Lambda, Dynamo, Step Functions, Athena).
• Cloud-based orchestration and resource management - AWS Hyperpods, Anyscale, Etc. Model Inference Orchestration.