Applied Scientist at Apple

San Diego, California, United States -

Full Time

Start Date

Immediate

Expiry Date

12 Jan, 26

Salary

0.0

Posted On

14 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Statistical Modeling, AI Framework Development, Python, C/C++, Java, Distributed Systems, Hadoop, Spark, Deep Learning, TensorFlow, PyTorch, Model Evaluation, Data Preprocessing, Collaboration, Problem Solving

Industry

Computers and Electronics Manufacturing

Description

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. 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. Hands-on experience in programming and implementing ML/AI models in Python or similar languages. Experience in building and maintaining machine learning pipelines, including data preprocessing, model training, and deployment. Demonstrated ability to develop and apply statistical and machine learning models for prediction, optimization, or causal analysis. Experience developing AI tools, frameworks, or APIs to support model deployment or LLM-based applications. 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).

Responsibilities

The Applied Scientist will design, develop, and implement sophisticated machine learning and AI models to solve complex problems. This role involves building end-to-end ML pipelines and collaborating with various teams to bring AI-powered solutions into production.