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Battery Machine Learning Algorithm Engineer

Apple· Hardware
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Hybrid (San Diego), Hybrid (Cupertino)$175k - $308.5k + bonusPosted 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, including SOH/SOP/SOC estimation, predictive models, and real-time optimization 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
  • Experience in C/C++ for embedded systems and Python & ML frameworks (PyTorch, TensorFlow)
  • PhD and 10+ years relevant industry experience
  • Mathematical modeling experience for nonlinear dynamical systems
  • Experience with physics-informed neural networks or hybrid physics/ML approaches
  • Experience with MPC implementation and real-time optimization

Skills

Required:Machine learningControl theory & optimizationMATLAB/SimulinkEmbedded C/C++Python & ML frameworksMathematical modelingMPC implementation
Nice to have:Physics-informed ML

Benefits

  • Comprehensive medical and dental coverage
  • Retirement benefits
  • Discounted products and free services
  • Education reimbursement for career advancement (tuition)
  • Potential discretionary bonuses or commission payments
  • Relocation assistance
Last updated Oct 23, 2026. Interested? Apply on the company site.

About Apple

Discover the innovative world of Apple and shop everything iPhone, iPad, Apple Watch, Mac, and Apple TV, plus explore accessories, entertainment, and expert device support.

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