Battery ML Engineer: On-Device Real-Time Control

Apple, Inc.; San Diego (CA); On-site; USD 142,000 - 214,000; Full time

Benefits offered by this job

Employee stock programs

Relocation assistance

Education reimbursement

Medical and dental coverage

Discretionary bonuses

Job summary

Apple, Inc. in San Diego, California, is seeking a Battery Machine Learning Algorithm Engineer to create novel ML algorithms for battery management, including SOH/SOP/SOC estimation, and to develop robust, thermally-aware control strategies for on-device operation.

You will design MPC and learning-based control, explore physics-informed ML, and deploy optimized models on resource-constrained hardware, collaborating across teams to ensure safe, efficient power delivery.

Qualifications

• Experience in machine learning (RNNs, LSTMs, Transformers) and control theory/optimization.
• Experience with MATLAB/Simulink.

Responsibilities

• Develop ML and adaptation algorithms for SOH/SOP/SOC estimation across temperature and aging.
• Build ML models for predictive battery dynamics and power demand.
• Design and implement MPC and other control strategies for real-time battery management.
• Collaborate with system teams to co-optimize battery algorithms with power/thermal systems.
• Develop physics-informed ML approaches to improve generalization.
• Deploy algorithms on embedded platforms using model compression and fixed-point arithmetic.

Skills

ML algorithms

control theory

optimization

MATLAB/Simulink

Python & ML frameworks

C/C++ for embedded

Education

PhD (preferred)

Tools

PyTorch

TensorFlow

MPC

Job description

Apple, Inc. in San Diego, California, is seeking a Battery Machine Learning Algorithm Engineer to create novel ML algorithms for battery management, including SOH/SOP/SOC estimation, and to develop robust, thermally-aware control strategies for on-device operation.

You will design MPC and learning-based control, explore physics-informed ML, and deploy optimized models on resource-constrained hardware, collaborating across teams to ensure safe, efficient power delivery.