Battery ML Engineer — On-Device, Real-Time MPC & Optimization

Apple Inc.

California

On-site

USD 185,000 - 325,000

Full time

14 days+

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Benefits offered by this job

Medical and dental coverage

Retirement benefits

Stock programs

Product discounts

Relocation support

Tuition reimbursement

Job summary

Apple Inc. is hiring a Battery Machine Learning Algorithm Engineer in Cupertino, CA. You will develop ML algorithms for SOH/SOP/SOC estimation, adaptive control, and physics-informed models for battery systems on-device.

Collaborate across hardware and software teams to optimize runtime, safety, and reliability while ensuring efficient power delivery in real-time. The role emphasizes deployment on constrained hardware with model compression and robust performance under sensor noise and

Qualifications

Experience in machine learning (RNNs, LSTMs, Transformers) and control theory for optimization.
Experience with embedded systems development using C/C++ for resource-constrained platforms.
Experience with MATLAB/Simulink and ML frameworks (PyTorch, TensorFlow).

Responsibilities

Develop ML and adaptation algorithms for SOH/SOP/SOC estimation and degradation mechanisms across temperature and aging.
Build ML-enhanced predictive models of user load patterns and coupled electro-thermal dynamics.
Design MPC, adaptive and learning-based control algorithms for real-time optimization of battery management.
Collaborate with system teams to co-optimize battery algorithms with power management and thermal systems.
Develop physics-informed ML methods to improve generalization with data-driven predictions.
Deploy algorithms on embedded platforms using quantization, pruning, distillation and fixed-point arithmetic.