Senior/Staff Machine Learning Engineer (Reinforcement Learning), Motion Planning

PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams.

Your opportunities joining PlusAI

Work, learn and grow in a highly future-oriented, innovative and dynamic field.

Wide range of opportunities for personal and professional development.

Catered free lunch, unlimited snacks and beverages.

Highly competitive salary and benefits package, including 401(k) plan.

Responsibilities

Design, develop, and deploy learned behavior planning models using approaches such as reinforcement learning, behavior cloning, and imitation learning; Own end-to-end Reinforcement Learning pipeline from environment construction, training, and modeling.; Contribute to Reinforcement Learning Infrastructure to facilitate large-scale distributed training; Track model training, analyze model performance, identify failure modes to improve generalization across driving scenarios; Collaborate with simulation and planning teams to deploy learned behavior planning models to driving pipelines

Requirements

MS or PhD in Computer/Software Engineering, Robotics, Machine Learning or related field.; Experience developing production code in C/C++ and Python; Experience with machine learning frameworks such as PyTorch; Experience with reinforcement learning; Strong oral and written communication skills.

Nice to have

Experience with high-throughput batched driving simulators such as GPUDrive and PufferDrive; Experience with self-play reinforcement learning; Experience with sim-to-real in the context of robotics / autonomous driving; Prior experience in learned behavior planning / prediction for autonomous vehicles