Machine Learning Engineer

About Lucid

At Lucid we are creating exceptional mobility experiences through innovation to drive the world forward. Built on Lucids proprietary technology and software-defined vehicle architecture our award-winning vehicles bring our Compromise Nothing approach to the global automotive market. That means refusing to choose between performance and sustainability design and engineering ambition and Lucid Air and Lucid Gravity we have designed and built vehicles that have redefined their segments combining exceptional range performance design and expansive space in a single experience.

We achieve this through deep vertical integration with design engineering and productionhappening in-houseacross our global offices the world united by a shared commitment to refusing to settle youcanhelpredefinewhatspossible andshape the future of mobility.

The GeoAI team is building next-generation AI-powered data automation platforms for ADAS and autonomous driving. We are seeking a Machine Learning Engineer to develop scalable auto-labeling technologies that transform fleet-collected sensor data into high-quality training datasets with minimal human intervention.

This role sits at the intersection of machine learning computer vision spatial AI and large-scale data infrastructure. You will work on perception models 3D scene reconstruction foundation models and automated data-generation systems that accelerate autonomous-driving development.

The Role:

Build Next-Generation Auto-Labeling Systems

• 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 that 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.

Develop Advanced Perception and Fusion Models

• Research train and evaluate camera-based 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 diverse driving environments and edge cases.
• Build end-to-end ML workflows from raw sensor ingestion and preprocessing through annotation quality control and dataset delivery.

Leverage Spatial AI and 3DGS

• Develop 3D Gaussian Splatting (3DGS) 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 Foundation Models to Data Automation

• Apply Vision-Language Models (VLMs) Vision-Language-Action (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.

Productionize ML Solutions

• 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.

Qualifications:

• 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.

Preferred:

• 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.