Machine Learning Engineer

Lucid Motors•Southfield, MI

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 convert fleet-collected sensor data into high-quality training datasets with minimal human intervention. The role spans 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 production-scale auto-labeling systems for camera and LiDAR data; develop perception and sensor-fusion models; use 3D Gaussian Splatting and related reconstruction methods; apply vision-language and other multimodal foundation models to data automation; and productionize scalable training and inference solutions for fleet-scale datasets. The role works across perception, mapping, navigation, simulation, hardware, logging, and data-platform teams and tracks operational metrics such as labeling accuracy, coverage, throughput, cost, and 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++.
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 and reduce 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-based and LiDAR-based detection, segmentation, tracking, depth-estimation, and occupancy models.
Develop multimodal fusion models that combine 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 that improve geometric accuracy and consistency across sensors, viewpoints, and time.
Apply Vision-Language Models, Vision-Language-Action models, and multimodal foundation models to scene interpretation, label generation, verification, and edge-case discovery.
Develop natural-language and agent-based workflows to 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.