Apple
Battery Machine Learning Algorithm Engineer
$142K–$225K
Cupertino, California +1 · Full-time · Posted Oct 20, 2026
- Location
- Cupertino, California
San Diego, California - Experience
- No minimum years specified; preferred: 3+ years relevant industry experience.
- Education
- PhD preferred.
About the job
Develops machine-learning algorithms, control strategies, and embedded implementations to improve battery estimation, runtime, efficiency, and safe power delivery.
What you'll do
- Develop algorithms to estimate battery State of Health, State of Power, and State of Charge, accounting for degradation and operating conditions.
- Build predictive models and real-time control and optimization methods for battery management, runtime, thermal behavior, and power delivery.
- Collaborate with system teams and deploy compressed algorithms on resource-constrained embedded platforms.
What they ask for
- Experience in machine learning, including RNNs, LSTMs, or Transformers.
- Experience in control theory and optimization, including MPC, Kalman filters, and convex/nonconvex optimization.
- Experience in MATLAB/Simulink.
Nice to have
- PhD and 3+ years relevant industry experience.
- Experience in nonlinear dynamical systems modeling, embedded C/C++, Python and ML frameworks, physics-informed or hybrid physics/ML methods, or real-time MPC.
Pay
$150,400 and $225,300 (base salary); $142,300 and $214,300 (base salary)
Tools
RNNs, LSTMs, Transformers, MPC, Kalman filters, MATLAB/Simulink, C/C++, Python
AI focus
Develops machine-learning and physics-informed/hybrid models for battery state estimation, user-load and power-demand prediction, and battery control; deploys compressed models on embedded platforms.
Model-written summary · the original posting is authoritative