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Location
In office (Cupertino)
Seniority
Staff · 10+ years exp
Visa
H-1B filings in 12 months: 6,350 · green card filings: 39

Confirmed on the employer's own hiring board on Nov 11, 2026. First seen by Alion on Nov 11, 2026. Apple scores A on the Alion truth index.

Overview
Company
Impact
Profile match
Apple is an American multinational technology company founded in 1976 by Steve Jobs, Steve Wozniak and Ronald Wayne, and headquartered in Cupertino, California. It designs and sells consumer hardware including the iPhone, Mac, iPad, Apple Watch, AirPods and Vision Pro, together with the operating systems and silicon that run them. A growing services division built around the App Store, iCloud, Apple Music, Apple TV+ and Apple Pay now contributes a large share of profit, making Apple one of the most valuable companies in the world.

Do you have a passion for invention and self-challenge? Do you thrive on pushing the limits of what’s considered feasible? As part of our Battery Engineering group, you’ll help craft creative battery algorithm and modeling solutions that deliver more energy in smaller spaces than ever before! Join us, and you’ll help us innovate new battery technologies that continually outperform the previous iterations. By collaborating with other product development groups across Apple, you’ll push the industry boundaries of what batteries can do and improve the product experience for our customers across the world.

Description

This position will develop battery physics-based models and machine learning algorithms to optimize battery performance.

Minimum Qualifications

MS degree.

Experience in electrochemical processes and performance / safety characterization.

Experience in physics-aware machine learning modeling and algorithms.

Experience in machine learning-based optimization techniques such as generic algorithm and gradient-based ML algorithms.

Preferred Qualifications

PhD and 10+ years relevant industry experience.

Experience in Matlab and/or Python.

Experience in mathematical modeling of linear and nonlinear dynamical systems and distributed parameter systems.

Experience deploying physics-based hybrid machine learning models into mass production products.

Understanding of current state of the art production processes applicable to lithium rechargeable cells.

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