Machine Learning Engineer, On-Device Adaptive Control

What you'll do

• Design and implement on-device control systems for thermal and energy management.
• Build and fit thermal models using lab and field data.
• Prototype MPC and related control algorithms 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.
• Integrate control systems into the OS by working with firmware, hardware, and platform teams.

What we're looking for

• MS or PhD in controls, robotics, electrical engineering, computer science, or a related field, or a BS with relevant experience.
• Experience with model predictive control, optimal control, or reinforcement learning for sequential decision-making.
• Strong programming skills in Python and comfort with C/C++ for on-device work.
• Experience working with real-world sensor data that is noisy, incomplete, or high-volume.
• Demonstrated ability to take a project from data exploration through a working prototype.
• Experience with thermal systems, battery management, or energy optimization (preferred).
• Familiarity with embedded or resource-constrained environments (preferred).
• Background in system identification, online parameter estimation, or shipping models into production (preferred).