ML Engineer

Summary

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.

Responsibilities

• Your duties include:
• - Build evaluation frameworks, including benchmarks, ground-truth datasets and graders. You'll measure precision, recall and false-positive rates, and drive them up.
• - Reduce the cost and latency of each run through better prompts, better design and the right model for each stage.
• - Build feedback loops that turn confirmed errors into better prompts, classifiers or training data.
• - Designing and building LLM-based systems that carry out quality tasks and independently grade their own results, including adversarial grading approaches that catch false "pass" results.
• - Designing classification pipelines that triage failures and separate real product issues from test or tooling problems.
• - Building datasets to evaluate, train or fine-tune models.
• - Writing clean, reviewable code, and helping the team keep a fast-moving codebase maintainable as it grows.

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.