Responsibilities

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.

Qualification

Scope of InfluenceBachelor’s Degree in Computer ScienceStrongStrong background in pose correctionTwo or more of the following at depth

Required

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 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; PhD in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 1+ 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 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.

Preferred

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

Senior, ML Engineer - Offline Perception at Torc Robotics · LumiJobs

Senior, ML Engineer - Offline Perception

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Torc Robotics

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