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Battery Machine Learning Algorithm Engineer

Apple· Hardware
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Hybrid (San Diego, CA), Hybrid (Cupertino, CA)$142.3k - $214.3kFull-timePosted 3d ago

About the role

Engineer to develop novel machine learning algorithms, control strategies, and on-device implementations to improve the utility and intelligence of battery systems, with emphasis on SOH/SOP/SOC estimation, predictive modelling, real-time optimization, and deployment on embedded platforms.

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 & ML frameworks (PyTorch, TensorFlow)
  • - Experience with physics-informed neural networks or hybrid physics/ML approaches
  • - Experience with MPC implementation and real-time optimization

Skills

Required:State of Health/State of Power/State of Charge estimationPredictive modelling of load patterns and electro-thermal dynamicsControl algorithms (MPC, adaptive, learning-based)Embedded systems in C/C++Python & ML frameworks (PyTorch, TensorFlow)
Nice to have:Physics-informed ML

Benefits

  • - Comprehensive medical and dental coverage
  • - Retirement benefits
  • - Discounts on products and free services
  • - Tuition reimbursement for education related to advancing your career
  • - Discretionary bonuses or commissions (where eligible)
  • - Relocation assistance (where eligible)
  • - Employee stock programs including RSUs and ESPP
Last updated Oct 23, 2026. Interested? Apply on the company site.

About Apple

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