Machine Learning Engineer - On-Device Control and Optimization

Job description

The Energy Tech org builds systems for managing the energy flow of Apple devices in service of a great user experience. 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. Our work directly impacts the behavior of Apple devices across the product families. Minimum Qualifications: 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 prototype Preferred Qualifications: Experience 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