Battery Machine Learning Algorithm Engineer

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

Envision what you could do here. At Apple, we believe new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there\"s no telling what you could accomplish.

We are looking for an engineer who will develop novel machine learning algorithms, control strategies, and on-device implementations to improve the utility and intelligence of battery systems.

Responsibilities

• Develop ML and adaptation algorithms for State of Health, State of Power, and State of Charge (SOH/SOP/SOC) estimation — characterizing degradation mechanisms (capacity fade, impedance growth) and peak power capability, with robustness across temperature, cell aging, dynamic loads, and inaccurate current sensing.


• Build ML-enhanced predictive models of user load patterns, system power demand, and coupled electro-thermal dynamics, enabling proactive, thermal-aware battery algorithms that extend runtime and improve power efficiency.


• Design advanced control algorithms (MPC, adaptive, learning-based) and multi-objective optimization frameworks to optimize battery management and power delivery in real time — balancing runtime, cycle life, thermal and system constraints, and safety margins.


• Collaborate with system teams to co-optimize battery algorithms with device power management and thermal systems.


• Develop physics-informed ML approaches that combine physics-based models with data-driven methods to improve generalization and ensure physically plausible predictions.


• Deploy algorithms on resource-constrained embedded platforms using model compression (quantization, pruning, distillation) and fixed-point arithmetic, with graceful fallback under sensor noise, missing data, and distribution shift.

Qualifications

• Experience in machine learning (RNNs, LSTMs, Transformers), control theory and optimization (MPC, Kalman filters, convex/nonconvex optimization)


• Experience in MATLAB/Simulink


• PhD and 3+ years relevant industry experience


• Mathematical modeling experience for nonlinear dynamical systems


• Experience in C/C++ for embedded systems and Python \\u0026 ML frameworks (PyTorch, TensorFlow)


• Experience with physics-informed neural networks or hybrid physics/ML approaches


• Experience with MPC implementation and real-time optimization