ML Engineer, Autonomous Driving Planning and Prediction

We are seeking a Machine Learning Engineer – Planning and Prediction (ADAS) to join our team 
This role is based in Newark, CA and requires employees to be onsite five days a week. 

Role: 

• Design, develop, and evaluate cutting-edge machine learning architectures for ADAS actor prediction, behavior modeling, and motion planning 

• Prototype, engineer, test, release, and launch ML-based features for ADAS and autonomous driving systems 

• Conduct research into state-of-the-art ML planning and prediction techniques and bring these innovations into production systems 

• Stay current with academic and industry advancements in planning and prediction, integrating promising methodologies into Lucid’s ADAS stack 

• Analyze data from simulation and fleet logs to identify and extract critical driving scenarios 

• Support data curation, storage, and transport workflows to facilitate automated inference and model development 

Required Qualifications: 

• Master’s or Ph.D. in Computer Science, Robotics, Machine Learning, or a related field 

• Expert-level proficiency in Python and ML libraries such as PyTorch or TensorFlow 

• Proficiency in C++ and strong hands-on experience with software engineering design principles 

• Proven experience deploying production systems that integrate large-scale ML models, evaluation pipelines, and performance metrics 

• Solid understanding of state-of-the-art techniques in perception, prediction, or planning for autonomous systems 

• Demonstrated experience designing scalable and efficient deep learning models 

• Understanding of traditional (non-ML) planning methods 

• Background in at least one of the following: perception, environment modeling, prediction, or planning in ADAS, autonomous vehicles, or robotic applications 

Preferred Qualifications: 

• 3+ years of experience in ML development, particularly in large-scale data and real-time systems 

• Academic or hands-on experience with imitation learning, reinforcement learning, or simulation-based training 

• Experience working with foundational models, large language models (LLMs), or end-to-end AV planning systems 

• Experience porting ML models to embedded platforms, with strong C++ fundamentals