Machine Learning Engineer – Motion Planning & Prediction

About the team

Our team develops the core software and data processing systems that power motion planning and decision-making in autonomous vehicles. We work at the intersection of machine learning, large-scale data infrastructure, and real-time vehicle control, collaborating across engineering, analytics, and product teams to deliver safe and intelligent driving capabilities.

About the role

We are looking for a creative & driven Machine Learning Engineer to join our autonomous vehicle team. You will be at the center of our efforts to build intelligent systems that can understand, predict, and safely navigate a complex and dynamic world. This role involves designing and training the next generation of deep learning models that form the brain of our vehicle, learning from petabytes of real-world driving data. If you are passionate about applying cutting-edge ML to solve high-stakes robotics challenges, we want to hear from you.

What you’ll do

• Design, train, and deploy state-of-the-art machine learning models for behavioral prediction and motion planning
• Develop robust data pipelines to process, clean, and label massive-scale vehicle sensor and simulation datasets
• Work with deep learning architectures such as transformers to model complex temporal interactions between traffic agents
• Establish and own the metrics for model performance, and create evaluation frameworks that correlate with on-road safety and performance
• Collaborate with software engineers to integrate and optimize trained models for real-time inference on the vehicles embedded hardware
• Stay current with the latest research in machine learning, imitation learning, and reinforcement learning, and apply novel techniques to our systems

What you’ll need

• Strong proficiency in Python and hands-on experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX)
• Solid understanding of machine learning fundamentals, including various neural network architectures, training methodologies, and evaluation techniques
• Experience with the full machine learning lifecycle, from data exploration and prototyping to deployment and monitoring
• Proficiency in C++ for writing high-performance model inference code

Nice to have

• A strong track record in ML competitions (e.g., Kaggle) or contributions to major open-source ML projects
• Experience applying ML to problems in robotics, such as behavioral prediction, motion planning, or computer vision
• Experience with MLOps tools and platforms (e.g., MLflow, Kubeflow, Weights & Biases)
• Experience with large-scale distributed data processing and training frameworks (e.g., Spark, Ray)
• Publications in top-tier ML or robotics conferences (e.g., NeurIPS, ICML, CVPR, ICLR, CoRL, RSS)