Perception Machine Learning Engineer – ADAS

Data & AIposted 4w ago

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.

Role and Responsibilities

Develop and optimize perception algorithms for autonomous driving systems using camera and LiDAR data.

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

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

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

Test, release, and deploy perception algorithms into Lucid production programs.

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

Propose innovative software algorithms to enhance future autonomous driving capabilities.

Required Qualifications

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

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 or other ML frameworks (e.g., TensorFlow, MXNet).

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 for a senior position.
Advanced degrees are preferred.
Experience in Automotive/Autonomous Driving background

Preferred Qualifications

Experience developing BEV transformer models for perception.

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.

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Compensation & Benefits: Lucid offers a comprehensive and competitive benefits package including medical, dental, and vision insurance; life and disability coverage; paid time off; paid holidays, paid sick leave; and a 401(k) retirement plan. Hourly/non-exempt employees accrue up to 120 hours paid time off, and salaried/exempt employee accrue up to 160 hours paid time off. Eligible employees may also participate in Lucid’s equity program and/or a discretionary annual cash incentive program. Incentive and equity awards, if applicable, are determined based on individual performance, role scope, market considerations, and overall company results, in accordance with the terms of the applicable plans.

Equal Opportunity: At Lucid, we believe diversity strengthens everything we build. Lucid Motors is proud to be an equal opportunity employer and is committed to providing an inclusive workplace for all. We consider all qualified applicants without regard to race, color, national or ethnic origin, age, religion, disability, sexual orientation, gender, gender identity or expression, marital status, or any other characteristic protected by applicable state or federal laws and regulations.

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