On-Device ML & Control Engineer for Energy Optimization

Socket.dev; Seattle (WA); On-site; USD 160,000 - 210,000; Full time

Job summary

Apple Energy Tech is seeking a Machine Learning Engineer to join the team building on-device control systems that optimize power and energy tradeoffs on Apple devices. You will analyze field data, prototype control and ML algorithms, and deploy models that operate within constrained hardware.

The role spans data analysis, modeling, and end-to-end deployment with an emphasis on practicality and robustness in real-world devices.

Qualifications

• MS or PhD in controls, robotics, electrical engineering, computer science, or other quantitative field.
• 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.

Responsibilities

• Analyze field data to understand device behavior and performance.
• Prototype control and ML algorithms and validate on-device viability.
• Ship control loops and ML models that run efficiently on constrained hardware.

Job description

Apple Energy Tech is seeking a Machine Learning Engineer to join the team building on-device control systems that optimize power and energy tradeoffs on Apple devices. You will analyze field data, prototype control and ML algorithms, and deploy models that operate within constrained hardware.

The role spans data analysis, modeling, and end-to-end deployment with an emphasis on practicality and robustness in real-world devices.