Sr/Staff Perception Machine Learning Engineer

We are looking for an experienced Perception Machine Learning Algorithm Engineer to join our ADAS/Autonomous Driving team. This position requires a highly skilled professional with a strong background in machine learning, computer vision, and perception algorithms, as well as solid programming expertise.

As a member of Lucid’s Perception team, you will research, design, implement, optimize, and deploy state-of-the-art machine learning models that advance perception algorithms for autonomous driving. You will conduct literature reviews, develop and modify models to enhance performance, and contribute to the deployment of these models in production vehicles.

Role and Responsibilities

• Develop and optimize perception algorithms for Level 2/2+/3 autonomous driving systems using camera, LiDAR and radar data.

• Design and implement cutting-edge deep learning algorithms for 2D/3D object detection, segmentation, tracking, and multi-task learning.

• Design, train, and evaluate machine learning or deep learning models for detecting vehicles, pedestrians, and other road users.

• Sensor fusion with data from camera, lidar, radar and/or inertial sensors.

• Research and integrate BEV-based transformer models for perception tasks.

• Collaborate with cross-functional teams to ensure seamless integration and robust implementation.

• Deploy, test and release perception algorithms into Lucid production programs.

• Support the validation and verification of perception algorithms using prototype and pre-production vehicles.

• Propose innovative software algorithms to enhance future autonomous driving capabilities.

• Strong theoretical foundations and expertise in deep learning algorithms, including dynamic and static object detection, tracking, and segmentation.

• Strong experience with 3D point cloud processing, with Lidar/Radar data.

• Proficient in Python with a focus on clean, efficient, and scalable software development.

• Comfortable working with large codebases and debugging complex machine learning models.

• Experience with PyTorch and deployment toolchains, ONNX, TensorRT.

• Ability to design and construct evaluation pipelines to unit-test ML models under diverse conditions and environments.

• Excellent communication skills and a strong team player.

• Bachelor’s degree in Computer Engineering, Electrical Engineering, Automotive Engineering, Mechanical Engineering, or a related field.

• Minimum of 3 years of relevant work experience, or a Ph.D. in a related field.

• Advanced degrees are preferred.

Preferred Qualifications

• Experience developing BEV transformer models for perception.

• Background in multi-sensor fusion.

• Proficiency in C++ with experience writing efficient, maintainable code.

• Practical, hands-on approach to solving complex problems in autonomous driving.

• Experience in testing and validating perception systems in real-world conditions.

• Experience working in agile development teams.

• Expertise in component and system integration, testing, and verification at the system and vehicle levels.

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.