ADAS Machine Learning Engineer

Leading the future in luxury electric and mobility At Lucid, we set out to introduce the most captivating, luxury electric vehicles that elevate the human experience and transcend the perceived limitations of space, performance, and intelligence. Vehicles that are intuitive, liberating, and designed for the future of mobility.   We plan to lead in this new era of luxury electric by returning to the fundamentals of great design – where every decision we make is in service of the individual and environment. Because when you are no longer bound by convention, you are free to define your own experience.   Come work alongside some of the most accomplished minds in the industry. Beyond providing competitive salaries, we’re providing a community for innovators who want to make an immediate and significant impact. If you are driven to create a better, more sustainable future, then this is the right place for you.

About the Role

We are looking for a highly skilled Machine Learning Engineer to join our ADAS (Advanced Driver Assistance Systems) and Autonomous Driving (AD) Data team. This role focuses on developing cutting-edge machine learning models that process and fuse multimodal sensor data—camera, LiDAR, radar—for 2D/3D perception and scene understanding. You will help align temporal data, deploy scalable models, and automate pipelines for training and evaluation.

 

Key Responsibilities

• Design, develop, and deploy machine learning models for sensor fusion, temporal alignment, and perception in ADAS/AD use cases.
• Integrate models into production workflows and debug performance issues related to misalignments, detection accuracy, and false positives.
• Build and maintain pipelines using MLFlow or similar tools to automate training, validation, deployment, and monitoring.
• Work with large-scale data collected from camera, LiDAR, radar, and vehicle telemetry to train and evaluate models.
• Collaborate with annotation, data platform, and front-end teams to ensure seamless integration of perception models.
• Drive improvements in object detection and scene understanding, both in 2D (camera) and 3D (LiDAR/Radar) domains.
• Utilize methods such as Ray Casting, 3D point cloud segmentation, and tracking to enhance detection performance and reduce latency.
• Contribute to optimizing active learning and sampling strategies to improve model generalization across edge cases.

 

Qualifications

• 2+ years of experience in automotive, robotics, or a related field, specifically in Perception or ML for ADAS/AD.
• Proven experience building models using multimodal sensor data (camera, LiDAR, radar).
• Deep understanding of object detection, sensor fusion, spatial-temporal modeling, and ray casting techniques.
• Proficiency in Python, PyTorch/TensorFlow, and deep learning frameworks.
• Experience with MLFlow, Kubeflow, or similar ML pipeline platforms.
• Hands-on experience with deploying models to production, debugging, and profiling for performance optimization.
• Solid understanding of 3D geometry, transformations, and calibration in the context of autonomous vehicles.
• Familiarity with tools such as ROS, OpenCV, Open3D, and visualization libraries.

Preferred Qualifications

• Experience working with HD maps, semantic segmentation, and tracking in autonomous driving environments.
• Familiarity with AWS/GCP for distributed training and inference pipelines.
• Experience collaborating with front-end and systems teams to integrate perception output into user-facing applications.

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