Dig into raw device logs and field data to build understanding of device behavior, find opportunities, and validate models Model device power and energy dynamics using lab and field data Develop and evaluate ML and control systems for on-device management Rapidly prototype end-to-end systems, from data analysis to device deployment, collaborating with firmware, hardware, and platform teams MS or PhD in controls, robotics, electrical engineering, computer science, or other quantitative field - or BS with relevant experience Experience with model predictive control, optimal control, or reinforcement learning (sequential decision-making) Experience working from raw logs or sensor data - comfortable building analysis from scratch Strong Python skills; demonstrated ability to take a project from data exploration through working prototypeExperience with thermal systems, battery management, or energy optimization Familiarity with embedded or resource-constrained environments Hands-on ML experience - training models, evaluating tradeoffs, iterating on approaches rather than applying off-the-shelf solutions Comfort with ambiguity - able to scope and drive work without detailed specifications Track record of shipping models or control systems into production, not just research. Within this org, the team develops end-to-end solutions utilizing on-device machine learning and control, creating new techniques from data analysis and prototyping.