ML Engineer

Apple, Inc.; Cork; On-site; EUR 120,000 - 180,000; Full time

Job summary

Apple Inc. in Cork, Ireland seeks an ML Engineer to apply LLMs for testing and releasing software to international users. You will own the model-facing side of AI-powered quality systems, designing, evaluating and improving them for reliability and efficiency.

You’ll collaborate with automation, quality and localization teams across time zones to ship better software to people worldwide.

Qualifications

• Experience building production applications on top of LLMs and tools.
• Hands-on prompt engineering and systematic model evaluation.
• Strong programming skills in Swift/Python and backends for ML apps.
• Ability to improve model behavior based on measured results.

Responsibilities

• Build evaluation frameworks with benchmarks, ground-truth data and graders.
• Reduce cost and latency with better prompts and model choices.
• Create feedback loops turning errors into improved prompts or data.
• Design and build LLM-based quality systems that autonomously grade results.
• Develop classification pipelines to triage failures and separate product issues from tooling problems.
• Build datasets for evaluation, training or fine-tuning models.
• Write clean, maintainable code for a fast-moving codebase.

Skills

LLM applications

Prompt engineering

Model evaluation

Swift/Python

Tools

Model serving infra

CI/CD tooling

Data pipelines

Open-weight models

Job description

Selection changes the language of the page/content

Cork, County Cork, Ireland Software and Services

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

• - 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.

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