Machine Learning Intern Remote Jobs in Cambridge, MA

Motional is a public transit and autonomous vehicle pioneer, developing Level 4 driverless vehicles that are changing the way the world moves. At the heart of our mission is the Autonomy organization, where we solve some of the most complex engineering and artificial intelligence challenges of our generation.
Mission Summary:

Motional is seeking a visionary, technically deep Director of Behaviors to lead our machine learning-based Prediction and Planning teams. In this role, you will sit at the intersection of intent forecasting and ego-vehicle decision-making. You will be directly responsible for leading multiple engineering sub-teams, setting the technical roadmap for our next-generation behavior stack, and pioneering the shift toward state-of-the-art end-to-end models that execute joint prediction and planning.

As a senior leader in the Autonomy organization, you will not only drive technical breakthroughs but will also scale and nurture a world-class AI organization in a sustainable, inclusive, and highly collaborative fashion.
Core Responsibilities:

• Strategic Leadership: Oversee and unify the machine learning-based Prediction and Motion Planning teams. Establish a clear, aggressive, yet sustainable technical roadmap that transitions our stack towards a unified (fully learnt) Large Driving Model performing joint prediction and planning.
• Technical Direction: Stay at the absolute frontier of AI research and define the technical roadmap for developing state-of-the-art imitation learning (IL) and reinforcement learning (RL) approaches to advance end-to-end learnt planning. Guide the team in exploring and incorporating modern paradigms like Vision-Language-Action models (VLAs) to improve the vehicle's semantic understanding, reasoning, and zero-shot generalization capabilities in complex urban environments. 
• Organizational Growth: Lead, mentor, and scale multiple sub-teams of machine learning engineers and researchers. Implement sustainable engineering practices that prevent burnout, promote psychological safety, and ensure high technical velocity.
• Cross-Functional Collaboration: Partner closely with Perception, Infrastructure and Systems Engineering to ensure the Large Driving Model seamlessly integrates onto the vehicle platform and meets rigorous safety and real-time performance standards.
  

• Proven Leadership: 5+ years of experience managing high-performing engineering teams, with at least 3+ years of experience managing multiple sub-teams within an autonomous systems, robotics, or advanced AI organization.
• Sustainable Scaling: Demonstrated track record of growing an engineering organization sustainably-balancing technical debt, architectural scalability, and team well-being.
• ML Behavior Expertise: Deep theoretical and practical proficiency in machine learning applied to robotics behaviors. Advanced expertise in Imitation Learning and Reinforcement Learning for decision-making. Strong understanding of the full lifecycle from research to vehicle deployment.
• Unified Architectures: Proven experience guiding teams toward building integrated models (e.g., trajectory forecasting joint with ego-policy generation) rather than decoupled, sequential pipelines.
• Modern AI Paradigms: Strong familiarity with multimodal foundational AI models, specifically Vision-Language-Action models (VLAs).
• Educational Background: M.S. or Ph.D. in Computer Science, Robotics, Electrical Engineering, or a related quantitative field with a heavy focus on Machine Learning.
  

• Experience building and scaling up LLMs/VLMs/VLAs and successfully deploying to production.
• A strong footprint in the AI/robotics research community (CVPR, ICCV, NeurIPS, ICRA, IROS publications), with a willingness to publish future work.
• Experience building large-scale data pipelines and training infrastructure required to train large driving models.
  

 We encourage a hybrid schedule with in-office time at one of our locations in Boston or Pittsburgh to support collaboration, or this role can be fully remote.