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 both lab and field data.
• Prototype Model Predictive Control (MPC) and related algorithms from analysis through deployment.
• Analyze large-scale field telemetry to characterize device behavior and validate models.
• Define and tune cost functions that encode system-level tradeoffs for on-device hardware.
• Integrate control systems into the operating system in coordination with firmware and hardware teams.

What we're looking 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 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).