Machine Learning Engineer - Intelligent Quality Systems

Our team of applied ML and software engineers builds intelligent systems that

help Apple's OS software ship faster and with higher quality. We apply

state-of-the-art ML, LLMs, computer vision, retrieval systems, and large-scale

data analysis throughout the software lifecycle. If you're excited by using ML

to solve real, large-scale problems in software quality, we have challenging and

meaningful work to do together.

DESCRIPTION

The Intelligent Quality Systems team designs and builds ML-powered systems that

transform how Apple approaches software quality at scale. We work across the

testing lifecycle: intelligently selecting which tests are most relevant to a

code change, automatically triaging test failures to their root cause,

validating UI and audio experiences with vision models, surfacing test coverage

gaps from change descriptions and defect history, and building the data

platforms that tie it all together. In this role, you'll bring ML ideas to life

on real-world, large-scale software systems. You'll prototype and evaluate

techniques for problems like change-impact prediction, LLM-powered failure

analysis, multimodal UI regression detection, and automated test recommendation.

You'll design data collection and evaluation strategies, work with large-scale

test result and code change data, and build systems robust enough to operate

reliably across one of the world's largest software engineering organizations.

You will be a crucial bridge between research ideas and production reality,

identifying gaps in data quality, modeling assumptions, and system design before

they become issues at scale. You'll succeed here if you enjoy turning ideas into

working ML systems, care deeply about measurement and rigorous evaluation, and

find it rewarding to see your work directly improve the productivity and quality

of software shipped to hundreds of millions of people.

MINIMUM QUALIFICATIONS

3+ years of experience with machine learning in academic or professional

settings, with demonstrated work on substantial ML projects beyond coursework

Strong programming and software engineering skills; ability to write clean,

maintainable, production-quality code Practical understanding of ML

fundamentals, model training, evaluation, and debugging, and ability to

implement algorithms from papers or specifications BS or MS in Computer Science,

Machine Learning, Statistics, or a related field

PREFERRED QUALIFICATIONS

Experience with NLP, large language models, code understanding, or

retrieval-augmented generation (RAG) Background in computer vision or multimodal

ML Experience building or maintaining data pipelines and ML infrastructure at

scale Familiarity with software testing, CI/CD systems, or developer tooling

Experience working with large-scale structured or semi-structured data (logs,

test results, code diffs) Comfort operating in environments where ground truth

labels are noisy, ambiguous, or expensive to obtain