Job description

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. 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 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

Requirements

Must-haves

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
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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlpython

Platform: apple
Effort: medium