At Apple, we believe that technology should enrich people's lives in meaningful ways. Apple Services-including the App Store, Apple Music, Apple TV+, and Podcasts-connect millions of users worldwide to the content and experiences they love. Behind these services are sophisticated AI and machine learning systems that power personalized recommendations, intelligent search, and content discovery. The quality of these experiences depends fundamentally on the integrity of the data that fuels them.
Join the Human-centered AI (HAI) organization within Apple Services Engineering as a Senior Applied Scientist on our Data Quality Operations team. This is your opportunity to shape the future of data quality at Apple, working at the intersection of cutting-edge machine learning, large language models, and rigorous scientific methodology. You'll be at the forefront of designing and implementing novel, scalable quality control solutions which ensure our AI-powered services meet Apple's exacting standards for a high-quality user experience. If you're passionate about applying scientific rigor to real-world problems, thrive on innovation, and want your work to impact hundreds of millions of users, this role offers an exceptional opportunity to make a lasting contribution to products people use every day.

Description:

We are seeking a Senior Applied Scientist to lead the design and implementation of novel, scalable quality control solutions for Apple Services. In this role, you will develop AI or ML solutions for programatically generating gold standard datasets, create anomaly detection systems, and pioneer use of Large Language Models for automated quality control. You will work closely with cross-functional teams to ensure the data powering our AI/ML systems meets the highest standards of accuracy, consistency, and relevance.

","responsibilities":"Develop methodologies and systems to programatically generating gold standard datasets to measure and improve data quality across diverse content types.

Build and validate LLM-based grading systems that align with human judgment for tasks including content classification, quality assessment, and metadata validation.

Create and deploy anomaly detection systems to proactively identify data inconsistencies, biases, and errors across production pipelines.

Collaborate with AI/ML engineering, product, and data science teams to identify quality challenges and integrate solutions into production workflows.

Stay abreast of the latest advancements in machine learning, AI, data quality, and LLM research, applying relevant techniques to enhance our capabilities.

Mentor and guide junior team members, fostering technical excellence and innovation in data quality operations.

Communicate findings and recommendations clearly to technical and non-technical stakeholders, including senior leadership.

Preferred Qualifications:

PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, NLP or a related quantitative field with 3+ years of relevant industry experience,

Experience with data labeling operations, annotation quality frameworks, or human-in-the-loop systems.

Familiarity with prompt optimization frameworks or automated prompt engineering.

Experience in natural language processing (NLP) or natural language understanding (NLU).

Experience with MLOps practices and tools for deploying, monitoring, and managing ML models in production.

Familiarity with cloud platforms (e.g., AWS, GCP, Azure) or large-scale distributed infrastructure systems.

Prior experience contributing to open-source projects or publishing research in relevant academic conferences or journals.

Minimum Qualifications:

3+ years of industry experience in an Applied Scientist, Machine Learning Engineer, or Data Scientist role with focus on data quality, evaluation, or related areas.

Proven experience designing, building, and deploying scalable data quality or evaluation systems in production environments.

Strong hands-on experience with Large Language Models (LLMs) including prompt engineering, fine-tuning, and applications such as grading, validation, or classification.

Proficiency in Python, data science libraries (pandas, Num Py, scikit-learn, Py Torch, Tensor Flow), SQL, and distributed computing environments (Spark, Hadoop).

MS in Computer Science, Machine Learning, Statistics, Applied Mathematics, NLP or a related quantitative field with 3+ years of relevant industry experience, OR BS degree in a related quantitative field with 8+ years of relevant industry experience.

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 .

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 $171,600 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.