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

Join our team as a Research Engineer or Senior Research Engineer, where you will develop and evaluate methods for accurate 3D object labeling using multi-sensor recordings. You will work on improving label quality, building and operating labeling pipelines at fleet scale, and collaborating with modeling teams. This position offers unlimited PTO, flexible working arrangements, health benefits, equity options, professional development opportunities, and daily catered lunches.

Responsibilities

• Develop and evaluate methods for turning multi-sensor recordings into accurate 3D object labels offline, and measure them against reviewed ground truth.
• Raise label quality and reduce the human review it needs by finding where machine labels fail and fixing the causes in models, sensor fusion, or data.
• Build and operate the labeling pipelines at fleet scale, including batch GPU inference, data extraction and alignment, and monitoring.
• 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

Requirements

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

Preferred

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

Pay

USD 130,000 - 220,000