On-Device ML & Control Engineer — Energy & Thermal

Apple Inc.; Seattle (WA); Hybrid; USD 142,000 - 214,000; Full time

Job summary

Apple Inc. Seattle location is seeking a Machine Learning Engineer to advance on-device control systems for thermal and energy management across Apple devices.

Work spans data analysis, modeling, and deploying control loops that run entirely on-device. You will prototype MPC and related algorithms, handle noisy field data, and collaborate with firmware, hardware, and OS teams to ship production-ready solutions.

Qualifications

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

Responsibilities

• Design and implement on-device control systems for thermal and energy management.
• Build and fit thermal models from lab and field data.
• Prototype MPC and related control algorithms end-to-end, from data analysis through on-device deployment.
• Analyze large-scale field telemetry to characterize device behavior and validate models.
• Define and tune cost functions that encode system-level tradeoffs.
• Collaborate with firmware, hardware, and platform teams to integrate control systems into the OS.

Skills

Model predictive control

Reinforcement learning

Python

C/C++

Sensor data

Data exploration

Education

MS/PhD in controls/robotics/EE/CS or BS with experience

Job description

Apple Inc. Seattle location is seeking a Machine Learning Engineer to advance on-device control systems for thermal and energy management across Apple devices.

Work spans data analysis, modeling, and deploying control loops that run entirely on-device. You will prototype MPC and related algorithms, handle noisy field data, and collaborate with firmware, hardware, and OS teams to ship production-ready solutions.