AIML - Data Scientist, Evaluation

Apple; Cupertino (CA); On-site; USD 147,400 - 272,100; Full time

Benefits offered by this job

Employee stock programs

Comprehensive medical coverage

Retirement benefits

Discounts on products and services

Job summary

A leading tech company is seeking a skilled Data Scientist to drive product impact via evaluation and measurement of AI products. You will design end-to-end evaluation frameworks and create high-quality datasets to inform product decisions. Ideal candidates have a strong background in data science and machine learning along with excellent collaboration skills. The role offers a competitive pay range of $147,400 to $272,100 and a comprehensive benefits package.

Qualifications

• Experience in data science, machine learning, and analytics.
• Strong programming skills in SQL and scripting languages.
• Excellent collaboration skills in cross-functional team environments.

Responsibilities

• Design evaluation frameworks for AI/ML systems.
• Build high-quality evaluation datasets and human-in-the-loop systems.
• Translate evaluation insights into actionable recommendations.

Skills

Data science

Machine learning

Statistical data analysis

A/B testing

Programming (Python, SQL)

Collaboration

Education

B.S. in Machine Learning, Computer Science, or related field

Tools

SQL

Python

R

Spark

Job description

Overview

Do you get excited by driving product impact via measurement and evaluation, for products and services used by hundreds of millions of people globally? The AIML Evaluation organization uses data as the voice of customers to improve products. The Data Science and Insight team informs product evolution through measurement, evaluation, and analysis of the user experience. You will partner with Apple Intelligence engineering teams to improve product quality and guide feature development with data, delivering experiences across iPhone, iPad, HomePod, Mac, Apple Watch, Apple TV, and dozens of languages.

Description

Research and develop evaluation methods to improve the quality of Apple Intelligence user-facing products. Collaborate with evaluation/experimentation engineering teams to translate methodological developments into technologies used by Apple Intelligence engineering.

Responsibilities

• Design and Own End-to-End Evaluation Frameworks: Develop rigorous evaluation methodologies for AI/ML systems, including metric definition, sampling strategy, experiment design, and statistical validity checks. Build scalable pipelines that ensure trustworthy, reproducible, and interpretable results across product surfaces and model iterations.
• Build High-Quality Evaluation Datasets & Human-in-the-Loop Systems: Create and maintain gold-standard datasets for offline and online model assessment. Lead data generation and annotation workflows (e.g., human ratings, Red Teaming, preference data, domain-specific evals), ensuring coverage, data quality, bias mitigation, and alignment with product and safety goals.
• Partner Cross-Functionally to Drive Model & Product Decision-Making: Translate evaluation insights into actionable recommendations for model training, ranking, and product launches. Collaborate with Research, Engineering, Product, and Safety teams to define quality bars, monitor regressions, optimize user experience, and guide roadmap prioritization.

Minimum Qualifications

• Experience in data science, machine learning, and analytics, including statistical data analysis and A/B testing.
• Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data.
• Strong programming skills, including data-querying skills (SQL and/or Spark, etc.) and experience with a scripting language for data processing and development (e.g., Python, R, or Scala).
• Excellent collaboration skills to achieve impactful results by working effectively with diverse cross-functional teams, including PMs, engineers, data scientists, and others.
• B.S. in Machine Learning, Computer Science, Statistics, Operations Research or other quantitative fields.

Preferred Qualifications

• Applicants have a good understanding of large language models (LLMs), including their architecture, training methods, prompt engineering and fine-tuning for specific tasks.
• Hands-on experience in applying LLMs to solve technical problems, such as data analysis, data automation, synthetic data generation, with proven ability to optimize model performance for accuracy and efficiency.
• Ph.D. in machine learning, computer science, statistics, operations research or other quantitative fields.
• 5 years of relevant work experience.

Pay & Benefits

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 $147,400 and $272,100, 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. They are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount through Apple’s Employee Stock Purchase Plan. Benefits include comprehensive medical and dental coverage, retirement benefits, discounts on products and services, and reimbursement for certain educational expenses for advancing your career at Apple. 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.

Apple accepts applications to this posting on an ongoing basis.