Machine Learning Engineer, Proactive

The AIML 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.

DESCRIPTION

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. This role will have the following

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

Master’s in Computer Science, Artificial Intelligence, Machine Learning, or a

related field 10 years of work experience in machine learning, deep learning or

related field 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 At least 10 years of

experience leading complex cross-functional projects and influencing research

direction At least 10 years of experience with large-scale model training,

optimization, and deployment 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