Machine Learning Engineer

Lucid Motors • Southfield, MI

16d

About The Position

Lucid’s GeoAI team is building AI-powered data automation platforms for ADAS and autonomous driving. The Machine Learning Engineer will develop scalable auto-labeling technologies that transform fleet-collected sensor data into high-quality training datasets with minimal human intervention. The role combines machine learning, computer vision, spatial AI, and large-scale data infrastructure, including perception models, 3D scene reconstruction, foundation models, and automated data-generation systems.

The engineer will design and deploy auto-labeling systems for camera and LiDAR data; develop perception and sensor-fusion models; build 3D Gaussian Splatting and related reconstruction pipelines; apply multimodal foundation models to data automation; and productionize scalable training and inference pipelines. The role involves collaboration with perception, mapping, navigation, simulation, hardware, logging, and data-platform teams to deliver end-to-end solutions and measure their accuracy, coverage, throughput, cost, and operational efficiency.

Requirements

• MS or PhD in Computer Science, Machine Learning, Robotics, Computer Vision, or a related field.
• Strong software-engineering skills in Python and/or C++, with hands-on experience using PyTorch or equivalent deep-learning frameworks.
• Solid understanding of deep learning, computer vision, 3D geometry, multi-view reconstruction, sensor fusion, and transformer architectures.
• Experience developing detection, segmentation, tracking, depth, occupancy, or multimodal perception models.
• Experience building, evaluating, and deploying machine-learning systems using large-scale datasets.
• Ability to work independently and collaborate effectively across research, software, data, and vehicle teams.

Nice To Haves

• Experience in autonomous driving, robotics, mapping, AR/VR, or spatial computing.
• Hands-on experience with 3DGS, NeRF, neural rendering, world models, or synthetic data generation.
• Experience building auto-labeling platforms, active-learning systems, human-in-the-loop workflows, or automated label-quality systems.
• Experience with camera-LiDAR fusion, depth estimation, 3D detection and tracking, and multimodal foundation models.
• Familiarity with VLMs, VLA models, SAM-family segmentation models, distributed computing, Docker, Kubernetes, CI/CD, and cloud ML infrastructure.

Responsibilities

• Design and deploy production-scale auto-labeling solutions for 2D and 3D object detection, semantic and panoptic segmentation, lane and road-structure extraction, multi-object tracking, occupancy, scene understanding, and traffic-participant behavior.
• Develop automated label validation, confidence scoring, consistency checks, and quality-monitoring frameworks.
• Build active-learning and human-in-the-loop workflows to improve label quality while reducing manual annotation effort and turnaround time.
• Create teacher-student and model-assisted labeling workflows for large-scale camera and LiDAR datasets.
• Research, train, and evaluate camera- and LiDAR-based detection, segmentation, tracking, depth-estimation, and occupancy models.
• Develop multimodal fusion models combining camera, LiDAR, radar, GNSS/IMU, and map context.
• Improve temporal consistency, calibration robustness, and long-tail performance across driving environments and edge cases.
• Build end-to-end ML workflows from raw sensor ingestion and preprocessing through annotation, quality control, and dataset delivery.
• Develop 3D Gaussian Splatting and related reconstruction pipelines for large-scale driving scenes collected from fleet vehicles.
• Use reconstructed scenes for label propagation, synthetic annotation generation, annotation validation, rare-case augmentation, and simulation.
• Create scene-centric representations to improve geometric accuracy and consistency across sensors, viewpoints, and time.
• Apply VLMs, VLA models, and multimodal foundation models to scene interpretation, label generation, verification, and edge-case discovery.
• Develop natural-language and agent-based workflows that help engineers search, inspect, curate, and validate large-scale datasets.
• Evaluate state-of-the-art research and convert promising methods into reliable production capabilities.
• Build scalable training and inference pipelines for fleet-scale datasets and distributed GPU infrastructure.
• Optimize model performance, throughput, reliability, monitoring, and reproducibility across cloud and production environments.
• Collaborate with perception, mapping, navigation, simulation, hardware, logging, and data-platform teams to deploy end-to-end solutions.
• Define and track metrics for labeling accuracy, coverage, throughput, cost, and operational efficiency.

Benefits

• Medical, dental, and vision insurance
• Life and disability coverage
• Paid time off
• Paid holidays
• Paid sick leave
• 401(k) retirement plan
• Eligible employees may participate in Lucid’s equity program and/or a discretionary annual cash incentive program.

What This Job Offers

• Job Type: Full-time