Research Engineer/Senior Research Engineer, 3D Auto-Labeling and Data Flywheel

Develop and evaluate methods that turn multi-sensor recordings into accurate 3D object labels offline, using models and compute the vehicle cannot afford in real time, and measure them against reviewed ground truth

Raise label quality and reduce the human review it needs: find where machine labels fail and fix the causes in models, sensor fusion or data; bring state-of-the-art perception and foundation models into offline use and adapt them to our vehicles and sensors

Build and operate the labeling pipelines at fleet scale: batch GPU inference, data extraction and alignment, selection of what goes to human review, annotation-tool integration, monitoring, throughput and cost, and regression tests that catch quality drops before data reaches training

Close the loop with the modeling teams: deliver training-ready labels, feed model failures and long-tail scenarios back into what gets curated and labeled next; at the senior level, lead designs and mentor

Ensure that your work is performed in accordance with the company's Quality Management System (QMS) requirements and contribute to continuous improvement efforts

BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a related technical field, or equivalent practical experience

Hands-on experience developing and evaluating 3D perception methods (detection, tracking, sensor fusion or related) with a framework such as PyTorch, including experimentation, error analysis, principled metrics, built on solid fundamentals in 3D geometry, coordinate frames, and camera and lidar calibration

Strong Python (NumPy, Pandas, SQL) and working C++ where performance matters; experience running large offline workloads with GPU batch inference

Foundations for data-centric machine learning: how training-data composition shapes model behavior, how to evaluate against ground truth, and how to measure and reduce annotation cost

Self-driven with a strong sense of responsibility: a quick learner who takes problems from ambiguous to shipped; for the senior level, a system you were responsible for end to end, design documents others built on, and mentoring

Auto-labeling, offline perception, or ground-truth generation for autonomous driving or robotics, including integrations with annotation tools and vendors

Experience improving models through data: active learning and data flywheels, hard-example mining, uncertainty or disagreement signals, dataset curation

Lidar SLAM, structure-from-motion, factor-graph optimization (GTSAM, Ceres), or stereo and monocular depth estimation

Foundation and multimodal models: open-vocabulary detectors, promptable segmentation, vision-language models, embeddings and vector search

Heavy-truck specifics (articulated vehicles, trailer state estimation, multi-platform calibration) or lane and road-geometry labeling pipelines