Machine Learning Engineer - On-Device Control and Optimization

Name: RiseMe
Availability: InStock

Seattle, WAOnsiteFull TimePosted 2 months agoVisa sponsorship available

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Required skills

on-device machine learning
control systems
power management
energy tradeoffs
device dynamics
cost functions
control loops
real-world conditions
noisy sensors
changing hardware

Nice to have

thermal systems
battery management
energy optimization
embedded environments
resource-constrained environments
ML training

Role summary

Apple's Energy Tech organization is seeking a Machine Learning Engineer to develop on-device control systems for managing power and energy on Apple devices. This role involves analyzing field data, prototyping control and ML algorithms, and implementing them on constrained hardware. The ideal candidate will have experience with model predictive control, optimal control, or reinforcement learning, strong Python skills, and the ability to build analyses from raw logs and sensor data. Familiarity with embedded environments, energy optimization, and hands-on ML experience is preferred.

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

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

Sample Apple interview questions

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Design a typeahead box for a search engine
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