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Home›Jobs›Apple›AIML - Staff Machine Learning Engineer
Apple

About Apple

The personal technology company redefining user experience

🏢 Tech, Hardware👥 1001+ employees📅 Founded 1976📍 Cupertino, CA⭐ 4.2
B2CB2BHardwareSaaSTelecommunicationseCommerce

Key Highlights

  • Market cap of $3 trillion as of 2022
  • Over 1 billion active devices worldwide
  • Comprehensive medical plans including mental healthcare
  • Paid parental leave and gradual return-to-work program

Apple Inc. (NASDAQ: AAPL), headquartered in Cupertino, CA, is the world's most valuable company with a market capitalization of $3 trillion as of 2022. Known for its iconic products such as the iPhone, iPad, and Mac, Apple serves over 1 billion active devices globally. The company has a strong commi...

🎁 Benefits

Apple offers comprehensive medical plans covering physical and mental healthcare, paid parental leave, and a gradual return-to-work program. Employees...

🌟 Culture

Apple's culture emphasizes an obsessive focus on user experience and consumer privacy, setting it apart from competitors. The company promotes inclusi...

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Apple

AIML - Staff Machine Learning Engineer

Apple • Seattle, Washington, United States

Posted 1 month ago🏛️ On-SiteLeadMachine learning engineer📍 Seattle
Apply Now →

Skills & Technologies

Machine learningDeep learningLarge language modelsPythonNatural Language ProcessingOpenAI API

Job Description

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 strengthening each other’s ideas. That happens because every one of us believes that we can make something wonderful and share it with the world, changing lives for the better! In this organization, we work hard to bring the best user experiences powered by Apple Intelligence. Our team is instrumental in powering and enhancing features across a range of Apple products, including Siri, Spotlight, Safari, Messages, and more. We are an Applied ML team pushing the limits of question answering, assistant response ranking, summarization, and search technologies, while also responsible for a production service. As part of this group, you will be doing large scale machine learning and deep learning research and development to improve Open Domain Question Answering (using both structured knowledge graph data and unstructured web data) and Summarization as well as developing fundamental building blocks needed for Artificial Intelligence. This involves developing sophisticated machine learning and large language models (LLMs) to understand user queries, retrieve and rank relevant documents across multiple sources and synthesize information across documents to provide user with a direct answer that best satisfies their intent and information seeking needs. Additionally, you will research and develop the state-of-the-art LLMs for summarizing personal data such as emails, messages, and notifications. You will also work with researchers and data scientists to develop, fine-tune, and evaluate domain specific Large Language Models for various tasks and applications in Apple’s AI powered products and conduct applied research to transfer the cutting edge research in generative AI to production ready technologies. In this role, you will work on LLM based question answering and Apple Intelligence features to provide concise, accurate, and grounded information to users to help them complete their tasks quickly on Apple devices. Your core responsibilities will include: * Designing and developing advanced Reinforcement Learning technologies in the post-training of generative model, and delivering the end-user experience. * Driving cross-functional technical initiatives, collaborating with research, engineering and production teams to translate theoretical advances into deployable systems. * Developing novel and cutting-edge RL algorithms and improving existing ones. * Staying up to date with the latest RL research and integrate best practices into the team's workflow. * Working on the end-to-end ML lifecycle: algorithm design and implementation, data collection, model training, evaluation, and deployment.

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