AI/ML - Machine Learning Engineer, Information Intelligence

Do you want to make Siri and Apple products smarter for our users? The Siri and Information Intelligence team is redefining how hundreds of millions of people use their devices to get information. We are an R&D team pushing the limits of question answering, assistant response ranking, and search technologies, while also responsible for a production service. We are part of a wider effort to power information across a variety of Apple products – including Siri, Spotlight, Safari, Messages, Lookup, and more. As part of our team, you will be leveraging and improving upon the latest deep learning techniques in order to understand queries, rank user intents, rank documents, and find useful answers to users’ questions. Our team is responsible for training and deploying these models at scale, using the latest advances in model compression and inference-optimized hardware.

Key Qualifications

• Experience in machine learning and deep learning, with a focus on natural language processing and information retrieval.

• Mastery of one of following languages: Python, Go, Java, C++.

• Strong coding skills and solid understanding of algorithms, data structures, and distributed system design.

• Excellent interpersonal skills and demonstrated ability to work independently as well as in a team.

• Excellent practical skills with and state of the art knowledge of major machine learning algorithms.

Description

As a member of our fast-paced group, you’ll have the unique and rewarding opportunity to shape upcoming products from Apple. Our team includes a diversity of backgrounds from applied scientists with a focus in NLP to experienced distributed systems engineers. As such, we are looking for candidates with applied machine learning experience and some engineering skills. This role will have the following responsibilities:

- Analyzing search ranking and relevance requirements issues and opportunities

- Utilizing PyTorch, TensorFlow, and JAX for training and deploying deep learning models

- Understanding product requirements then translating them into modeling tasks and engineering tasks

- Building machine-learned models for search relevance ranking query understanding and questioning

- Publishing and present in several top NLP conferences like EMNLP and NAACL