Senior Machine Learning Applied Researcher

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something! The Commerce & Growth Intelligence team at Apple Services Engineering is driving innovation across the full user lifecycle—spanning account creation, marketing, personalized offers, subscription ranking, churn prediction, lifetime value optimization, and more. We are hiring for a role within the ML Applied Research team, which is responsible for building and deploying end-to-end machine learning solutions that power key business outcomes in acquisition, engagement, and retention. This is a hands-on role where you will work with large-scale datasets, develop and productionize models, and collaborate closely with product, engineering, analytics, and other cross-functional partners to ship impactful ML-driven features. The work spans a broad range of modeling techniques—from XGBoost models used in financial risk scoring, to transformer-based architectures for personalization, and LLM-powered components integrated into user-facing systems. However, this is not a dedicated LLM fine-tuning role—while experience with LLMs is valued, this role requires practical experience in applying a variety of ML approaches to real-world problems at scale. We expect a 60–70% focus on applied ML and feature launches, with the remaining 20–30% dedicated to research, including ideation, prototyping, technical demos, publications, and patent contributions.

We are seeking a talented and experienced Senior Applied Researcher to lead and contribute to high-visibility projects. This role requires deep expertise in machine learning, a passion for research and development, and the ability to effectively communicate complex ideas to cross-functional teams and partners. If you are a self-motivated, energetic individual with a track record of delivering impactful results, we want to hear from you!

In this role, you will:

• Design and implement advanced machine learning models to optimize user experiences across Apple’s ecosystem, including App Store, Subscription services, and marketing campaigns.
• Collaborate with cross-functional teams to translate business objectives into technical solutions.
• Lead research and development initiatives, including LLM fine-tuning and the exploration of emerging AI technologies.
• Analyze large-scale datasets to uncover actionable insights, improve model performance, and drive business impact.
• Develop and evaluate prototypes, proof-of-concepts, and production-ready solutions.
• Communicate findings and recommendations effectively to technical and non-technical audiences, including leadership and business partners.
• Contribute to the team’s research goals by authoring publications and filing patents in alignment with Apple’s innovation standards.
• Mentor junior researchers and foster a culture of collaboration, innovation, and excellence within the team.

• Ph.D. in Machine Learning, Computer Science, Mathematics, Statistics, or a related field, or equivalent work experience
• Advanced expertise in machine learning, deep learning, and data mining techniques.
• Proficiency in Python and/or Scala for building, training, and deploying machine learning models for complex, high-dimensional systems.
• Experience with large-scale distributed data processing frameworks such as Spark or Hadoop.
• Strong programming skills and experience with production-level algorithm development.
• Exceptional communication skills, with the ability to present technical concepts and results to diverse audiences.
• Highly self-motivated and results-driven, with a proven ability to thrive in fast-paced, dynamic environments.

• Familiarity with reinforcement learning and advanced optimization techniques.
• Experience with personalization systems, recommendation engines, or ranking algorithms.
• Strong understanding of state-of-the-art LLMs and deployment for real-world applications.
• Knowledge of marketing analytics, user segmentation, and customer lifetime value modeling.
• Prior experience in payment science, subscriptions, or App Store analytics.
• A strong history of paper publications in well-known journals or top-tier conferences in machine learning and AI.

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 $181,100 and $318,400, 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.

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.