Motional is seeking a distinguished Machine Learning Engineer to join its Autonomy team in Behaviors. As a member of our team you will develop key features for our autonomous driving platform. In this role, you will apply machine learning to a mix of prediction, planning, and control problems. The candidate is required to have hands-on experience and deep knowledge in machine learning. Understanding of deep neural networks, autonomous vehicles, and domain adaptation is highly desirable. You should have strong research, SW development, communication, interpersonal, and analytical skills. 

What you’ll be doing:

• Researching, implementing, and evaluating deep-learning-based methods for prediction and planning for Motional’s Autonomous Vehicle products.
• Leading, Designing, running, and analyzing experiments and testing to evaluate the efficiency of our solutions on real-world data.
• Partnering with system software engineering specialists to ship industrial strength ML models.
• Communicating and collaborating with multi-functional teams.

What We're Looking For: 

• BS/MS/PhD in computer science, electrical engineering, mechanical engineering, applied math, or related fields (or equivalent experience)
• 15+ years of proven experience building ML systems for autonomous vehicles or similar robotics applications
• Deep understanding of large language models (LLM) and transformers
• Hands on experience building large scale production ML systems and deploying them at scale
• Excellent leadership and track record for innovation to help us build the next generation of our ML based planning solutions
• Experience with deep neural network (DNN) training, inference and optimization in leading frameworks (Pytorch, Tensorflow, TensorRT, etc.).
• Excellent understanding of the mathematical foundations of machine learning and deep learning.

Bonus Points: 

• Prior experience as a ML planning lead
• Proven publication record in ML for planning, vision, or related fields
• Prior experience building and deploying vision language action or chain of thought models