Machine Learning Engineer - On-Device Control and Optimization
Apple · Seattle ·
- Category
- ML ai
- Company size
- 1000+
Develops on-device ML models and control systems to optimize power/energy tradeoffs in Apple hardware, analyzing real-world device behavior and shipping solutions to manage thermal/battery dynamics across products.
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.
Description We are developing on-device control systems that manage power and energy tradeoffs on Apple devices. This means building models that capture device dynamics, designing cost functions that encode explicit priorities, and shipping control loops that adapt to real-world conditions.
We're looking for a Machine Learning Engineer who can work across the full stack: analyzing field data to understand device behavior, prototyping control and ML algorithms, and getting them running on-device. The problems are messy — noisy sensors, changing hardware, competing objectives — and the solutions need to be simple enough to ship on constrained hardware.
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