Machine Learning Engineer - Intelligent Quality Systems
21 days ago Cupertino, CA
About us
Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.
- Size: 10,000+ employees
- Industry: Technology: Manufacturing
Why you should apply for a job to Apple:
Ranked as one of the Best Technology Companies in 2020
3.7/5 in overall job satisfaction
3.1/5 in supportive management
66% say women are treated fairly and equally to men
65% would recommend this company to other women
66% say the CEO supports gender diversity
Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›
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.","responsibilities":"Implement and refine ML solutions across the testing lifecycle, test selection, failure triage, visual validation, and test coverage analysis
Rapidly prototype multiple algorithmic approaches to identify the most promising directions
Design and implement data collection, labeling, and evaluation strategies for training and measuring ML systems
Work with large-scale test result and code change data to surface patterns and inform modeling decisions
Proactively identify research-to-production gaps and technical risks in proposed ML solutions
Collaborate with applied scientists and software engineers to ship reliable, high-impact ML systems
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
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
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
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.","responsibilities":"Implement and refine ML solutions across the testing lifecycle, test selection, failure triage, visual validation, and test coverage analysis
Rapidly prototype multiple algorithmic approaches to identify the most promising directions
Design and implement data collection, labeling, and evaluation strategies for training and measuring ML systems
Work with large-scale test result and code change data to surface patterns and inform modeling decisions
Proactively identify research-to-production gaps and technical risks in proposed ML solutions
Collaborate with applied scientists and software engineers to ship reliable, high-impact ML systems
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
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
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Additional Information
- Salary: $150,400 – $277,600
- Posted: 21 days ago