Machine Learning Engineer On-Device Adaptive Control

The Energy Tech org builds systems for managing the energy flow and thermals of Apple devices in service of a great user experience. Within this org the team develops end-to-end solutions utilizing on-device machine learning and control creating new techniques from data analysis and prototyping. Our work directly impacts the behavior of Apple devices across the product families.

We are developing on-device control systems that manage thermal and energy tradeoffs on Apple devices. This means building models that capture device dynamics designing cost functions that encode explicit priorities and shipping control loops that adapt to real-world conditions. Were looking for a Machine Learning Engineer who can work across the full stack: analyzing field data to understand device behavior prototyping control and ML algorithms and getting them running on-device. The problems are messy noisy sensors changing hardware competing objectives and the solutions need to be simple enough to ship on constrained hardware.

Design and implement on-device control systems for thermal and energy managementnBuild and fit thermal models from lab and field datanPrototype MPC and related control algorithms end-to-end from data analysis through on-device deploymentnAnalyze large-scale field telemetry to characterize device behavior and validate modelsnDefine and tune cost functions that encode system-level tradeoffsnCollaborate with firmware hardware and platform teams to integrate control systems into the OS

MS or PhD in controls robotics electrical engineering computer science or related field or BS with relevant experiencenExperience with model predictive control optimal control or reinforcement learning (sequential decision-making)nStrong programming skills in Python; comfort with C/C for on-device worknExperience working with real-world sensor data (noisy incomplete high-volume)nDemonstrated ability to take a project from data exploration through working prototype

Experience with thermal systems battery management or energy optimizationnFamiliarity with embedded or resource-constrained environmentsnBackground in system identification or online parameter estimationnComfort with ambiguity able to scope and drive work without detailed specificationsnTrack record of shipping models or control systems into production not just research