Machine Learning Engineer – Motion Planning & Prediction

<h2 class="heading-2">About the team</h2> <p class="p1">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.</p> <h2 class="heading-2">About the role</h2> <p class="p1">We are looking for a creative &amp; 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.</p> <h2 class="heading-2">What you'll do</h2> <ul> <li>Design, train, and deploy state-of-the-art machine learning models for behavioral prediction and motion planning</li> <li>Develop robust data pipelines to process, clean, and label massive-scale vehicle sensor and simulation datasets</li> <li>Work with deep learning architectures such as transformers to model complex temporal interactions between traffic agents</li> <li>Establish and own the metrics for model performance, and create evaluation frameworks that correlate with on-road safety and performance</li> <li>Collaborate with software engineers to integrate and optimize trained models for real-time inference on the vehicles embedded hardware</li> <li>Stay current with the latest research in machine learning, imitation learning, and reinforcement learning, and apply novel techniques to our systems</li> </ul> <h2 class="heading-2">What you'll need</h2> <ul> <li>Strong proficiency in Python and hands-on experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX)</li> <li>Solid understanding of machine learning fundamentals, including various neural network architectures, training methodologies, and evaluation techniques</li> <li>Experience with the full machine learning lifecycle, from data exploration and prototyping to deployment and monitoring</li> <li>Proficiency in C++ for writing high-performance model inference code</li> </ul> <h2 class="heading-2">Nice to have</h2> <ul> <li>A strong track record in ML competitions (e.g., Kaggle) or contributions to major open-source ML projects</li> <li>Experience applying ML to problems