Machine Learning Engineer - On-Device Adaptive Control

Socket.dev; Seattle (WA); On-site; USD 150,000 - 190,000; Full time

Job summary

Apple Energy Tech engineers are building on-device control systems to optimize thermal and energy tradeoffs in Apple devices. We develop models, cost functions, and control loops that run within constrained hardware, shipping reliable solutions across product families.

The machine learning engineer will analyze field data, prototype control and ML algorithms, and bring them from data exploration to working on-device implementations in a highly interdisciplinary team.

Qualifications

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

Responsibilities

• Analyze field data to understand device behavior
• Prototype control and ML algorithms
• Ship on-device control loops across Apple devices
• Collaborate across cross-functional teams

Skills

MPC

Reinforcement learning

Python

C/C++

Sensor data

On-device ML

Prototyping

Education

MS/PhD in controls, robotics, EE, CS or related field; or BS with relevant experience

Tools

Python

C/C++ toolchain

Job description

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.

Description

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. We're 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.

Minimum 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

Preferred Qualifications

• Experience with thermal systems, battery management, or energy optimization
• Familiarity with embedded or resource-constrained environments
• Background in system identification or online parameter estimation
• Comfort with ambiguity — able to scope and drive work without detailed specifications
• Track record of shipping models or control systems into production, not just research