AIML - Staff Machine Learning Engineer, Answers Knowledge and Information

1 Minute ago • 10 Years + • Research Development • $212,000 PA - $386,300 PA

Job Summary

Job Description

Apple is seeking a Staff Machine Learning Engineer to join the AIML team, focusing on powering and enhancing features across Apple products like Siri, Spotlight, and Safari. This applied ML role involves large-scale machine learning and deep learning R&D to improve Open Domain Question Answering and Summarization, utilizing structured and unstructured data. The engineer will develop sophisticated ML and LLMs to understand user queries, retrieve and rank relevant documents, and synthesize information for direct answers. Additionally, the role includes researching and developing state-of-the-art LLMs for summarizing personal data and transferring cutting-edge generative AI research into production-ready technologies.
Must have:
  • Designing and developing advanced Reinforcement Learning technologies in the post-training of generative model
  • Delivering the end-user experience
  • Driving cross-functional technical initiatives, collaborating with research, engineering and production teams
  • Translating 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
  • Work on LLM based question answering and Apple Intelligence features
  • Provide concise, accurate, and grounded information to users
  • Help users complete tasks quickly on Apple devices
Good to have:
  • Deep expertise in reinforcement learning-based post-training on LLM models
  • Reward modeling
  • RLHF
  • RLAIF
  • Chain-of-thought
  • Agentic AI R&D
  • Deep understanding of cutting edge RL algorithms and large language model
  • Deep understanding in LLM pre-training, post-training
  • Strong product intuition and ownership
  • Excellent communication skills
Perks:
  • Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
  • Eligible for discretionary restricted stock unit awards
  • Can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan
  • Comprehensive medical and dental coverage
  • Retirement benefits
  • A range of discounted products and free services
  • Reimbursement for certain educational expenses — including tuition
  • Eligible for discretionary bonuses or commission payments
  • Relocation

Job Details

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.

Core Responsibilities

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.

Qualifications

  • 10+ years of ML experiences in search, natural language processing/understanding. Conversational AI.
  • Proven experience for LLM post training, including but not limited to SFT, RLHF, RLAIF, Reward Modeling, Chain-of-thought, agentic LLM.
  • Hands-on experience building RL pipelines and training agents in simulation or real-world environments.
  • Growth mindset and ability to learn new technologies
  • MS or Ph.D. in Computer Science, Machine Learning with a specialty in reinforcement learning, or a related field

Preferred Qualifications

  • Deep expertise in reinforcement learning-based post-training on LLM models, reward modeling, RLHF, RLAIF, Chain-of-thought, and agentic AI R&D.
  • Deep understanding of cutting edge RL algorithms and large language model.
  • Deep understanding in LLM pre-training, post-training.
  • Strong product intuition and ownership
  • Excellent communication skills

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