Machine Learning Engineer

About Lucid At Lucid, we are creating exceptional mobility experiences through innovation to drive the world forward. Built on Lucid’s 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 integrity. In 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 production happening in-house across our global offices and manufacturing facilities. Our teams come from industries around the world, united by a shared commitment to excellence. By refusing to settle, you can help redefine what’s possible and shape 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, cloud infrastructure, and large-scale data engineering. You will work on perception models, 3D scene reconstruction, foundation models, distributed ML systems, and automated data-generation platforms that accelerate autonomous-driving development. You will help design, deploy, and operate production-scale machine learning workloads on AWS, supporting the ingestion, processing, training, inference, validation, and delivery of petabyte-scale vehicle and sensor data. The Role: Build Automated Data Labeling Systems • Develop scalable auto-labeling solutions for camera and LiDAR data, including object detection, segmentation, tracking, occupancy, and scene understanding. • Create label validation, quality monitoring, and active-learning workflows to improve accuracy and reduce manual effort. • Design cloud-based data processing pipelines for large-scale autonomous vehicle datasets. Develop Perception & Sensor Fusion Models • Train and optimize camera, LiDAR, and multimodal perception models for detection, tracking, segmentation, and occupancy estimation. • Build fusion systems leveraging camera, LiDAR, radar, GNSS/IMU, and map data. • Improve model performance across diverse driving environments and edge cases. Apply Foundation Models & AI Automation • Leverage VLMs, VLAs, and multimodal foundation models for data annotation, validation, scene understanding, and edge-case discovery. • Build AI-powered workflows that streamline dataset search, curation, and quality assurance. • Evaluate and productionize emerging AI research. Build & Scale ML Infrastructure • Design and operate cloud-native ML platforms on AWS for data processing, distributed training, and inference. • Develop containerized workloads using Kubernetes, Docker, and Infrastructure-as-Code tools. • Implement monitoring, security, CI/CD, and cost-optimization best practices for large-scale ML systems. Productionize Machine Learning Solutions • Build and deploy scalable training and inference pipelines for fleet-scale datasets. • Establish MLOps practices for model versioning, monitoring, evaluation, and deployment. • Partner cross-functionally to deliver reliable, end-to-end ML and data solutions. Qualifications: • MS or PhD in Computer Science, Machine Learning, Robotics, Computer Vision, Cloud Computing, Distributed Systems, 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, deploying, and operating machine-learning systems using large-scale datasets. • Hands-on experience with AWS or a comparable cloud platform, including compute, object storage, container orchestration, monitoring, and identity and access management. • Experience with Docker, Kubernetes, distributed computing, and production ML or data-processing pipelines. • Understanding of cloud architecture, infrastructure automation, system observability, reliability, security, and cost optimization. • Ability to work independently and collaborate effectively across research, software, data, cloud-platform, 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. • Experience deploying large-scale training and inference workloads using AWS services such as S3, EC2, EKS, SageMaker, AWS Batch, Lambda, Step Functions, Glue, and CloudWatch. • Experience operating distributed GPU workloads and optimizing GPU utilization, scheduling, autoscaling, fault tolerance, and infrastructure cost. • Experience with infrastructure-as-code tools such as Terraform, AWS CloudFormation, or AWS CDK. • Experience with CI/CD, MLOps, experiment tracking, model registries, data versioning, and production observability. • Familiarity with cloud networking, IAM, encryption, secrets management, data governance, and security controls. • Familiarity with VLMs, VLA models, SAM-family segmentation models,