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In office (Herzliya)
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Middle · 3+ years exp
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Confirmed on the employer's own hiring board on Nov 10, 2026. First seen by Alion on Nov 10, 2026. Apple scores A on the Alion truth index.

Overview
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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.

Our team is new, growing, and evolving quickly, and we are hiring across multiple disciplines. We work at the intersection of machine learning, hardware, and software, creating technologies that reshape how people communicate and experience the world around them in everyday life.

Description

We are looking for a scientist who can turn experimental results into deep physical insight. Working closely with experimental teams gathering raw data, you will analyze multi-source datasets to uncover the connections between signals, derived features, and system-level performance metrics. You will combine data-driven modeling with first-principles physical intuition to explain performance dynamics and isolate the root causes driving them.

Minimum Qualifications

MSc or PhD in physics, electrical engineering, or a related quantitative field.

3+ years of relevant industry experience.

Strong signal processing background (time-frequency analysis, noise, coherence, filtering).

Experience with large-scale data analysis

Strong understanding of experimental physics and measurement systems, enough to interpret results and spot artifacts.

Solid statistics: hypothesis testing, regression, uncertainty estimation, and feature selection.

Ability to work independently on open-ended problems.

Preferred Qualifications

Machine learning frameworks (scikit-learn, PyTorch) and model interpretability techniques.

Experience with optical, photonic, or sensor data.

Distributed or high-volume data processing.

Occasional hands-on lab experience.

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