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)
Candidates are required to be authorized to work in the U.S. The employer is not offering relocation sponsorship, and remote work options are not available.