On-Device ML & Control Engineer - Thermal & Energy

Socket.dev; Seattle (WA); On-site; USD 150,000 - 190,000; Full time

Job summary

Apple Energy Tech engineers are building on-device control systems to optimize thermal and energy tradeoffs in Apple devices. We develop models, cost functions, and control loops that run within constrained hardware, shipping reliable solutions across product families.

The machine learning engineer will analyze field data, prototype control and ML algorithms, and bring them from data exploration to working on-device implementations in a highly interdisciplinary team.

Qualifications

• MS or PhD in controls, robotics, EE, CS, or related field — or BS with relevant experience
• Experience with model predictive control, optimal control, or reinforcement learning
• Strong programming skills in Python; comfort with C/C++ for on-device work
• Experience with real-world sensor data (noisy, incomplete, high-volume)
• Demonstrated ability to take a project from data exploration through working prototype

Responsibilities

• Analyze field data to understand device behavior
• Prototype control and ML algorithms
• Ship on-device control loops across Apple devices
• Collaborate across cross-functional teams

Skills

MPC

Reinforcement learning

Python

C/C++

Sensor data

On-device ML

Prototyping

Education

MS/PhD in controls, robotics, EE, CS or related field; or BS with relevant experience

Tools

Python

C/C++ toolchain

Job description

Apple Energy Tech engineers are building on-device control systems to optimize thermal and energy tradeoffs in Apple devices. We develop models, cost functions, and control loops that run within constrained hardware, shipping reliable solutions across product families.

The machine learning engineer will analyze field data, prototype control and ML algorithms, and bring them from data exploration to working on-device implementations in a highly interdisciplinary team.