Applied Scientist

We're idealists. Inventors. Forever tinkering with products and processes, always on the lookout for better. Whether you work at our global offices, offsite, or even at home, a job at Apple will be demanding. But it also rewards forward-thinking, creative thinking and hard work. And none of us here would have it any other way. Does an exciting, dynamic, and fast-paced environment catch your attention? Do you like puzzles and determining solutions that are not obvious? Terrific! Consider joining our team! The Applications team is looking for an outstanding Applied Scientist who will strengthen our team's capabilities in statistical modeling, machine learning, and AI framework development. This role will drive innovation in building scalable ML and AI solutions that enhance our product intelligence, improve automation, and expand our AI-driven capabilities across business domains.

Description

The Applied Scientist will work on designing, developing, and implementing sophisticated machine learning and AI models to solve complex product, engineering, and business problems. The role involves building end-to-end ML pipelines, developing AI tools and APIs, and collaborating closely with engineering, product, and marketing partners to bring intelligent, data-driven, and AI-powered solutions into production. The ideal candidate combines deep technical expertise in machine learning, statistical modeling, and AI framework development with strong problem-solving and interpersonal skills, ensuring effective collaboration and measurable impact in a fast-paced environment.

Preferred Qualifications

Experience with LLM fine-tuning, prompt engineering, or retrieval-augmented generation (RAG).

Familiarity with generative AI techniques (e.g., diffusion models, transformer architectures).

Experience building scalable ML systems using cloud platforms (AWS, GCP, or Azure) and ML Ops tools (e.g., SageMaker, Vertex AI, MLflow).

Understanding of data engineering and feature pipeline automation.

Experience developing or contributing to AI frameworks, APIs, or internal tools used by other teams.

Strong software engineering practices, including version control, testing, and code review.

Familiarity with cross-domain applications of AI/ML (e.g., marketing analytics, personalization, recommendation systems).

Minimum Qualifications

PhD in Statistics, Computer Science, Mathematics, or a related quantitative field with 1+ year of relevant experience; or MS with 4+ years of experience in applied machine learning, statistical modeling, or AI development.

1+ years experience and programming proficiency with Python. Alternatively, we may consider C/C+, Java, etc.

1+ years experience and working proficiency with distributed systems: Hadoop, Spark, etc.

1+ years experience and working proficiency with statistical modeling and machine learning algorithms for supervised and unsupervised learning, including classification, regression, clustering, etc.

Working familiarity with causal inference models.

Working familiarity with deep learning algorithms: CNN/RNN/LSTM/Transformer, etc, and deep learning frameworks like TensorFlow or PyTorch.

Knowledge of model evaluation, validation techniques, and performance metrics.

Ability to translate research ideas into scalable, production-level ML/AI solutions.

Excellent analytical, communication, and collaboration skills across multi-functional teams.