Strong proficiency in Python and experience with frameworks like PyTorch, TensorFlow, or JAX
Solid understanding of machine learning fundamentals and neural network architectures
Experience managing the machine learning lifecycle from data exploration to deployment
Proficiency in C++ for developing high-performance inference code
Responsibilities
Design, train, and deploy advanced machine learning models for vehicle behavior prediction and motion planning
Build data pipelines for processing and labeling extensive vehicle sensor data
Utilize deep learning architectures to model interactions among traffic agents
Establish performance metrics and evaluation frameworks linked to safety
Collaborate with software engineers to enhance model integration and real-time performance
Stay updated on machine learning advancements and integrate cutting-edge techniques
Benefits
Opportunity to work on cutting-edge technology in autonomous vehicles
Collaborative work environment with cross-disciplinary teams
Focus on real-world application of machine learning and AI
Engagement with large-scale challenges in data processing and robotics
Potential for career growth within a rapidly advancing field
Full Job Description
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.
