Developing core software and data processing systems to power motion planning and decision-making in autonomous vehicles at the intersection of machine learning, large-scale data infrastructure, and real-time vehicle control
Requirements
- 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
- 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)
Responsibilities
- 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
Other
- Bachelor's or Master's degree in Computer Science, AI, Statistics, or a related technical field
- 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
- A strong track record in ML competitions (e.g., Kaggle) or contributions to major open-source ML projects
- Publications in top-tier ML or robotics conferences (e.g., NeurIPS, ICML, CVPR, ICLR, CoRL, RSS)
