Senior/Staff Machine Learning Engineer, 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. We’re looking for a machine learning engineer to train and deploy the latest generation of ML-based planning algorithms on the extensive data we collect every day across our autonomous trucking fleet.   Responsibilities

Use complex map and perception sensor output to develop novel deep learning models for our planning stack.

Design critical safety checks to ensure driving trajectories are feasible and follow rules of road.

Ensure all model development keeps a real-time focus and operates efficiently in compute-constrained environments.

Track and incorporate the latest multidisciplinary research advancements in a fast-moving field. 

Ensure that your work is performed in accordance with the company’s Quality Management System (QMS) requirements and contribute to continuous improvement efforts.

Ensure that technical work meets customer requirements, regulatory standards, and company quality policies.

Required Skills

BS, MS, or PhD in Software, Robotics, or related field

4+ years of machine learning engineering experience for robotics applications

Experience developing high-quality software from design and implementation to testing and deployment.

Expertise with Python, willingness to do some C++ development as needed.

Experience training with modern frameworks (e.g. PyTorch)

A rigorous approach to model development: running well-designed experiments, defining suitable training and validation datasets, and evaluating on the right metrics.

Hands-on familiarity with cloud data ingestion pipelines

Strong communication and collaborative skills