Machine Learning Engineer - On-Device Adaptive Control

What you'll do

• Design on-device thermal and energy control systems, including thermal models and tuned cost functions.
• Prototype control algorithms from data analysis through deployment; analyze field telemetry to characterize behavior and validate models.
• Collaborate with firmware, hardware, and platform teams to integrate control systems into the OS.

What they ask for

• MS or PhD in controls, robotics, electrical engineering, computer science, or a 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 and comfort with C/C++ for on-device work.
• Experience with real-world sensor data and demonstrated ability to take a project from data exploration through working prototype.

Nice to have

• Experience with thermal systems, battery management, or energy optimization; familiarity with embedded or resource-constrained environments.
• Background in system identification or online parameter estimation; experience shipping models or control systems into production.

Pay

$142,300 and $214,300 (base salary)

Tools

Python, C/C++, MPC

AI focus

On-device machine learning for adaptive thermal and energy control, including modeling device dynamics, prototyping control algorithms, and deploying them on constrained hardware.