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Confirmed on the employer's own hiring board on Nov 8, 2026. First seen by Alion on Nov 6, 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.

Apple's Software Localization & Release Engineering team makes sure our software works well for people in every language and region. We're looking for an ML Engineer to apply LLMs and machine learning to how we test and release software for our international users.

You'll own the model-facing side of AI-powered quality systems: designing LLM-based solutions, evaluating their accuracy, and improving them. Your goal is to make these systems reliable, efficient and trustworthy. You'll work on a fast-moving effort with real internal users that depend on it.

Description

You'll work alongside automation, quality and localization engineers, and with international quality engineers, translators and project managers across several time zones. Your work will help Apple ship better software to people around the world.

Minimum Qualifications

Experience building production applications on top of LLMs, including tool use, structured output or agentic designs.

Hands-on experience with prompt engineering and systematic model evaluation, such as writing rubrics or graders and measuring precision and false-positive rates.

Strong programming skills in Swift / Python, and experience building backends for ML applications.

Ability to improve model behavior iteratively, based on measured results.

Preferred Qualifications

Hands-on experience fine-tuning or training models.

Experience with computer vision or object detection, such as detecting elements in screenshots or documents.

Experience with open-weights models and local inference.

Experience with multi-agent systems.

Familiarity with LLM serving infrastructure, such as model gateways, retries and cost-aware model selection.

Experience building feedback loops that turn model failures into improved prompts, classifiers or training data.

Familiarity with software test automation concepts, such as test execution, pass/fail grading and CI/CD.

Experience in software localization or internationalization.

Experience mentoring other engineers.

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