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

Plus

. Develop and evaluate methods that turn multi-sensor recordings into accurate 3D object labels offline .

Posted 10/26/2026full-timeSanta Clara • California • United StatesSenior💰 $130,000 - $220,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and evaluating 3D perception methods, including sensor fusion and auto-labeling for autonomous vehicles. Proficient in Python and C++, with hands-on experience in GPU batch inference and data curation processes.

Highest-signal resume keywords

3D Perception MethodsSensor FusionPyTorch FrameworkPython ProgrammingGPU Batch Inference

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

3D GeometryCamera CalibrationLidar CalibrationDetectionTrackingSQLNumPyPandasC++Auto-Labeling

Soft Skills

MentoringContinuous Improvement

Tools & Technologies

GPUAnnotation ToolsData ExtractionMonitoring SystemsRegression Testing

Industry Keywords

Autonomous DrivingRoboticsGround Truth GenerationData FlywheelsHeavy-Truck Specifics

Tech Stack

Tools & technologies

NumpyPandasPythonPyTorchSQLC++

About the role

Key responsibilities & impact

Develop and evaluate methods that turn multi-sensor recordings into accurate 3D object labels offline
Measure auto-generated labels against reviewed ground truth
Improve label quality and reduce human review by identifying failures in models, sensor fusion, or data
Bring state-of-the-art perception and foundation models into offline use and adapt them to company vehicles and sensors
Build and operate fleet-scale labeling pipelines, including batch GPU inference, data extraction and alignment, human-review selection, annotation-tool integration, monitoring, throughput, cost, and regression testing
Deliver training-ready labels and feed model failures and long-tail scenarios back into data curation and labeling
At the senior level, lead designs and mentor
Perform work in accordance with the company's Quality Management System requirements and contribute to continuous improvement
Develop 3D obstacle auto-labeling for autonomous-truck multi-sensor recordings, with potential expansion into other data-flywheel areas

Requirements

What you’ll need

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, including detection, tracking, or sensor fusion
Experience with PyTorch or a similar framework
Solid fundamentals in 3D geometry, coordinate frames, camera calibration, and lidar calibration
Strong Python, including NumPy, Pandas, and SQL
Working C++ where performance matters
Experience running large offline workloads with GPU batch inference
Understanding of how training-data composition shapes model behavior
Ability to evaluate against ground truth and measure and reduce annotation cost
For senior level: end-to-end system ownership, design documents others built on, and mentoring
Preferred: auto-labeling, offline perception, or ground-truth generation for autonomous driving or robotics
Preferred: active learning, data flywheels, hard-example mining, uncertainty or disagreement signals, and dataset curation
Preferred: lidar SLAM, structure-from-motion, factor-graph optimization, GTSAM, Ceres, stereo or monocular depth estimation
Preferred: foundation and multimodal models, open-vocabulary detectors, promptable segmentation, vision-language models, embeddings, and vector search
Preferred: heavy-truck specifics or lane and road-geometry labeling pipelines

Benefits

Comp & perks

Wide range of opportunities for personal and professional development
Catered free lunch
Unlimited snacks and beverages
Highly competitive salary and benefits package
401(k) plan