Battery ML Engineer - On-Device AI & Control

Apple Inc.; Cupertino (CA); On-site; USD 180,000 - 280,000; Full time

Job summary

Apple Inc. is seeking an engineer to develop novel ML algorithms and on-device implementations to enhance battery system intelligence, performance, and reliability.

You will design ML-enhanced estimators for SOH/SOP/SOC, build predictive models for power demand, and craft MPC-based control strategies that operate in real time on embedded hardware. The role requires deep expertise in ML (RNNs/LSTMs/Transformers) and control theory, plus experience with MATLAB/Simulink and embedded C/C++.

Qualifications

• PhD required with 3+ years industry experience.
• Strong modeling for nonlinear dynamical systems.
• Experience with ML and control theory in battery systems.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow).
• C/C++ for embedded systems.
• Experience with physics-informed neural networks.
• Experience with MPC and real-time optimization.

Responsibilities

• Develop ML/adaptation algorithms for SOH/SOP/SOC estimation across temperature and aging.
• Build ML-enabled predictive models for power demand and electro-thermal dynamics.
• Design advanced control algorithms (MPC, adaptive, learning-based) for real-time battery management.
• Collaborate with system teams to co-optimize battery algorithms with power and thermal systems.
• Develop physics-informed ML approaches for physically plausible predictions.
• Deploy algorithms on resource-constrained embedded platforms with model compression and fixed-point arithmetic.

Skills

ML algorithms

RNNs/LSTMs/Transformers

Control theory

MPC

Kalman filters

Embedded ML (fixed-point)

Hybrid physics/ML

Education

PhD in Electrical Engineering or related field

Tools

MATLAB/Simulink

C/C++ for embedded systems

Python

PyTorch

TensorFlow

MPC implementation

Job description

Apple Inc. is seeking an engineer to develop novel ML algorithms and on-device implementations to enhance battery system intelligence, performance, and reliability.

You will design ML-enhanced estimators for SOH/SOP/SOC, build predictive models for power demand, and craft MPC-based control strategies that operate in real time on embedded hardware. The role requires deep expertise in ML (RNNs/LSTMs/Transformers) and control theory, plus experience with MATLAB/Simulink and embedded C/C++.