Battery Machine Learning Algorithm Engineer

Apple, Inc.; Cupertino (CA); Presencial; USD 185.000 - 325.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Comprehensive medical and dental

Employee stock purchase plan

Relocation assistance

Tuition reimbursement

Discounted products and services

Retirement benefits

Descripción de la vacante

Apple, Inc. in Cupertino seeks an engineer to develop novel machine learning algorithms for battery systems, including on-device implementations and robust control strategies such as MPC.

You will design physics-informed ML approaches and deploy on resource-constrained embedded platforms. You will collaborate with system teams to co-optimize battery algorithms with power management and thermal systems, and you will work with MATLAB/Simulink, C/C++, Python and ML frameworks to bring models from

Formación

• Experience in machine learning (RNNs, LSTMs, Transformers).
• Experience in control theory and optimization (MPC, Kalman filters, convex/nonconvex).
• Experience with MATLAB/Simulink.
• Experience in C/C++ for embedded systems and Python & ML frameworks (PyTorch, TensorFlow).

Responsabilidades

• Develop ML and adaptation algorithms for SOH/SOP/SOC estimation with robustness across temperature, aging, loads and sensor noise.
• Build ML-enhanced predictive models of user load patterns and electro-thermal dynamics for proactive battery control.
• Design advanced control algorithms (MPC, adaptive, learning-based) and multi-objective optimization for real-time battery management.
• Collaborate with system teams to co-optimize battery algorithms with power management and thermal systems.
• Develop physics-informed ML approaches that combine physics-based models with data-driven methods.
• Deploy algorithms on resource-constrained embedded platforms using model compression and fixed-point arithmetic.

Conocimientos

Machine learning (RNNs, LSTMs, Transf.

Control theory & optimization (MPC, KF

MATLAB/Simulink

C/C++ for embedded systems

Python & ML frameworks (PyTorch, TF)

Educación

PhD

Descripción del empleo

Battery Machine Learning Algorithm Engineer

Cupertino, California, United States Hardware

Envision what you could do here. At Apple, we believe new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.

Description

We are looking for an engineer who will develop novel machine learning algorithms, control strategies, and on-device implementations to improve the utility and intelligence of battery systems.

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.

Minimum 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)

Preferred Qualifications

• 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

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $324,800, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

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