The role involves building scalable systems for training and fine-tuning large-scale generative models to produce realistic driving behaviors. The individual will lead the implementation of reinforcement learning algorithms and reward functions to generate high-fidelity driving insights. Responsibilities include developing deep learning models and generative AI solutions to automate workflows and analyze self-driving behavior. The position requires overseeing the production and optimization of machine learning models to assess the performance of a large vehicle fleet. Additionally, the role involves monitoring industry practices to develop data collection and evaluation systems based on reinforcement learning from human preference. Collaboration with various technical teams and leadership is essential to deliver on strategic initiatives.

Candidates must possess an M.S. or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience. A minimum of 7 years of hands-on experience in developing and applying machine learning models, specifically with a focus on reinforcement learning, is required. Applicants must demonstrate expertise in deep learning, sequence modeling, and generative models, alongside proficiency in Python and standard frameworks like JAX or TensorFlow. The role requires a proven ability to lead complex technical projects and experience with large-scale distributed training. A strong publication record or history of impactful project delivery is also necessary.