Apple Inc.
Machine Learning Engineer, AI/ML Search Platform, Apple Services Engineering
Apple Inc., Seattle, Washington, us, 98127
Overview
Machine Learning Engineer, AI/ML Search Platform, Apple Services Engineering — Seattle, Washington, United States Software and Services Are you passionate about building scalable, reliable search platforms at scale? Join Apple Media Products and help create the best music search experience across Apple platforms. The Apple Media Search Platform team supports millions of users and billions of transactions across hundreds of countries and languages. If you are excited about delivering the best search results when users ask to play music on iPhone, Apple TV, Apple Watch, HomePod, this is the right place to be. We obsess over search experience across the Apple ecosystem and other ecosystems where Apple Music is supported. Description
As part of our team, you will enhance thousands of compute and big data pipelines to deliver greater scalability, reliability, and efficiency. By leveraging innovative approaches with machine learning and large language models, you will improve pipeline quality, optimize Spark and Kubernetes resource utilization, and create automation that accelerates developer agility. Responsibilities
Develop automation and LLM-based agents to automatically increase testing coverage for data pipelines in a monorepo environment. Develop automation and LLM-based agents to optimize Spark job resource utilization, including both CPU and memory efficiency. Develop LLM-powered agents to automatically diagnose failures in large-scale data pipelines. Build tools and automation to accelerate engineer productivity across development, testing, and production deployment of new pipelines. Design and maintain dashboards to improve observability of pipeline execution and verification. Deliver cost-efficient solutions for storage and compute platform migrations through automation and advanced machine learning techniques. Minimum Qualifications
Bachelor’s degree in Computer Science, Computer Engineering, or a related field. 3+ years of experience with large-scale data processing and pipelines. Proficiency in Scala, Python, and scripting languages. Experience in and solid understanding of distributed systems, performance tuning, and resource optimization. Strong hands-on expertise with Apache Spark and the Hadoop ecosystem. Preferred Qualifications
Experience developing or applying machine learning techniques or LLM-based agentic workflows for data pipeline optimization and data quality improvements. Knowledge of cost optimization strategies for big data infrastructure. At Apple, base pay is one part of our total compensation package and is determined within a range. The base pay range for this role is between $139,500 and $258,100, 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. Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
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Machine Learning Engineer, AI/ML Search Platform, Apple Services Engineering — Seattle, Washington, United States Software and Services Are you passionate about building scalable, reliable search platforms at scale? Join Apple Media Products and help create the best music search experience across Apple platforms. The Apple Media Search Platform team supports millions of users and billions of transactions across hundreds of countries and languages. If you are excited about delivering the best search results when users ask to play music on iPhone, Apple TV, Apple Watch, HomePod, this is the right place to be. We obsess over search experience across the Apple ecosystem and other ecosystems where Apple Music is supported. Description
As part of our team, you will enhance thousands of compute and big data pipelines to deliver greater scalability, reliability, and efficiency. By leveraging innovative approaches with machine learning and large language models, you will improve pipeline quality, optimize Spark and Kubernetes resource utilization, and create automation that accelerates developer agility. Responsibilities
Develop automation and LLM-based agents to automatically increase testing coverage for data pipelines in a monorepo environment. Develop automation and LLM-based agents to optimize Spark job resource utilization, including both CPU and memory efficiency. Develop LLM-powered agents to automatically diagnose failures in large-scale data pipelines. Build tools and automation to accelerate engineer productivity across development, testing, and production deployment of new pipelines. Design and maintain dashboards to improve observability of pipeline execution and verification. Deliver cost-efficient solutions for storage and compute platform migrations through automation and advanced machine learning techniques. Minimum Qualifications
Bachelor’s degree in Computer Science, Computer Engineering, or a related field. 3+ years of experience with large-scale data processing and pipelines. Proficiency in Scala, Python, and scripting languages. Experience in and solid understanding of distributed systems, performance tuning, and resource optimization. Strong hands-on expertise with Apache Spark and the Hadoop ecosystem. Preferred Qualifications
Experience developing or applying machine learning techniques or LLM-based agentic workflows for data pipeline optimization and data quality improvements. Knowledge of cost optimization strategies for big data infrastructure. At Apple, base pay is one part of our total compensation package and is determined within a range. The base pay range for this role is between $139,500 and $258,100, 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. Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
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