Battery Machine Learning Algorithm Engineer

Apple Inc.; San Diego (CA); On-site; USD 142,000 - 214,000; Full time

Benefits offered by this job

Medical and dental coverage

Educational expense reimbursement

Employee stock programs

Relocation assistance

Job summary

Apple Inc. seeks an Battery Machine Learning Algorithm Engineer to develop novel ML algorithms for battery health and power estimation, and on-device implementations to enhance system intelligence and efficiency.

You will design advanced control strategies (MPC, adaptive, learning-based) and optimize battery management in real time, collaborating with cross-functional teams to balance runtime, safety, and thermal constraints while ensuring robust performance across temperatures and aging.

Qualifications

• Experience with ML models for battery health and power estimation.
• Experience with on-device ML and edge deployment.
• Experience with MATLAB/Simulink.

Responsibilities

• Develop ML algorithms for SOH/SOP/SOC estimation and battery aging.
• Build ML predictive models for load patterns and electro-thermal dynamics.
• Design MPC/adaptive/learning-based control and real-time optimization.
• Collaborate with system teams to co-optimize battery and thermal management.
• Develop physics-informed ML approaches for robust generalization.
• Deploy algorithms on resource-constrained embedded platforms with model compression.

Skills

RNNs/LSTMs/Transformers

Control theory

Optimization

MPC

Education

PhD in relevant field

Tools

MATLAB/Simulink

Python (PyTorch, TensorFlow)

C/C++ for embedded

Job description

Battery Machine Learning Algorithm Engineer

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

Preferred Qualifications

• 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

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 $142,300 and $214,300, 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

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

Learn about accessibility in Apple’s workplace

Learn about reasonable accommodations for job applicants

Apple accepts applications to this posting on an ongoing basis.