Machine Learning Engineer, Answers Knowledge and Information - Proactive

Summary

The Information Intelligence team is creating groundbreaking technology for artificial intelligence, machine learning and natural language processing! The features we create are redefining how hundreds of millions of people use their computers and mobile devices to search and find what they are looking for.

Our universal search engine powers search features across a variety of Apple products, including Siri, Spotlight, Safari, Messages and Lookup. We also develop state-of-the-art generative AI technologies based on Large Language Models to power innovative features in both Apple’s devices and services on the cloud.

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.

About the role

As a member of our fast-paced group, you’ll have the unique and rewarding opportunity to shape upcoming products from Apple. We are looking for highly motivated machine learning engineers and researchers having strong machine learning and deep learning fundamentals with hands-on experience in fine-tuning deep learning and large language models.

Responsibilities

• Conduct research and development on state-of-the-art deep learning and large language models for various tasks and applications in Apple’s AI-powered products
• Developing, fine-tuning, and evaluating domain-specific Large Language Models for various NLP tasks including summarization, question answering, search relevance/ranking, entity linking and query understanding problems
• Conducting applied research to transfer the cutting edge research in generative AI to production ready technologies
• Understanding product requirements, translate them into modeling tasks and engineering tasks
• Stay up to date with the latest advancements and research in deep learning and large language models

Minimum Qualifications

• Experience in modeling user behavior including personalization, online learning and recommendation systems
• Experience working with machine learning or LLM model development for various NLP tasks and RAG applications including prompt engineering, training data collection and generation, model fine-tuning and model evaluation
• Experience working with Python and at least one of the deep learning frameworks such as TensorFlow, PyTorch, or JAX

Preferred Qualifications

PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

At least 2 years of experience in various state-of-the-art techniques related to LLM fine-tuning in 1 or more of the following areas -- Supervised Fine-tuning (SFT) with Rejection Sampling, Preference-based fine-tuning techniques (e.g RLHF, Reward model, DPO, PPO, GRPO etc.), Parameter efficient fine-tuning techniques (e.g LoRA), Hallucination reduction and factual accuracy improvements, Designing and implementing safety guardrails

• Consistent track record of researching, inventing and/or shipping advanced machine learning models
• Outstanding communication and interpersonal skills with ability to work with cross-functional teams