ML Research Engineer, Interpretable AI for End-to-End Automated Driving

• Conduct research on interpretable AI methods for end-to-end learned automated driving policies, under the guidance of senior and staff researchers. • Develop and evaluate structured representations of driving behavior, such as interpretable behavioral modes underlying learned neural policies. • Implement methods that associate driving behavior with perceptual and contextual cues, including language-based or symbolic explanations where appropriate. • Design and run experiments using large-scale learned policies and simulation infrastructure to assess interpretability, diagnostic value, and failure modes. • Contribute to evaluations of explainability methods for debugging, validation, and analysis of learned driving systems in simulation and/or controlled datasets. • Collaborate with researchers and engineers across AD2, LBM, and WFM teams to integrate xAI ideas into broader research workflows. • Document research findings clearly and contribute to internal reports, technical presentations, and peer-reviewed publications. • Stay up to date with advances in interpretable AI, representation learning, generative models, and embodied AI research.

• Master's or PhD or equivalent research experience in Machine Learning, Robotics, Computer Vision, or a related quantitative field. • A demonstrated ability to conduct independent research and contribute to peer-reviewed publications at leading venues (e.g., NeurIPS, ICML, ICLR, CVPR, CoRL, RSS, ICRA).Strong foundation in modern machine learning, including deep learning, representation learning, and sequence or policy modeling. • Experience implementing and evaluating ML models using Python (and familiarity with C++ in research or experimental contexts). • Interest in or experience with end-to-end learning approaches for robotics or autonomous systems. • Ability to work effectively in collaborative, cross-disciplinary research environments. • Strong written and verbal communication skills. • Experience with interpretable AI, or model introspection techniques. • Familiarity with structured or hybrid models (e.g., latent-variable models, program induction, or discrete representations). • Experience evaluating learning-based systems in closed-loop simulation or real-world embodied settings. • Background in automated driving, robotics, or safety-critical AI systems.