Machine Learning Technical Lead - Data Engineering, MLO

Do you believe Machine Learning and AI can change the world? We truly believe it can! We are the ML Data Ops team at Apple. We build high quality ML datasets, from very small targeted sets to petabyte scale, to train and evaluate ML models that power AI-centric features for many Apple products. Our datasets help power intelligent algorithms on Camera and the Photos app, improve text input experiences (autocorrect, autocompletion, OCR) and more recently feed generative technologies in Apple Intelligence (Image Playground, Genmoji, Writing Tools, Math Notes…) We’re looking for an exceptional engineering lead who is passionate about Apple products and values; who loves working with data ops at scale; and who is committed to the hard work vital to continuously improve complex ML data pipelines and data infrastructure. AI-centric products are the future of software; at their core, data is the source code of AI, a key component of innovation and inclusive and fair ML products. We invite you to join us at this exciting time; grow fast and positively impact multiple critical features on your first day at Apple!

Description

Our Data team focuses on acquiring, synthesizing, annotating, and ensuring the quality of ML data, driving numerous features in collaboration with R&D teams in Apple’s SWE organization. As a data engineering tech lead, you will be responsible for establishing and executing the strategy for our organization’s ML data engine.

Minimum Qualifications

Bachelors, Masters or PhD in Computer Science, Mathematics, Physics, or a related field; or equivalent practical experience.
7+ years of industry experience as a software engineer, including 2+ years as a tech lead/architect specialized in data infrastructure

Preferred Qualifications

Proven experience designing, automating and scaling large data pipelines (petabyte scale desired) using state of the art technologies
Expertise in Python or another modern programming language
Strong ability to design and lead a technical roadmap, work with cross functional teams and a diversity of profiles, proven capacity to influence and build alignment
Demonstrated prior experience in foundation models such as building data infrastructure supporting generative technologies
Self-starter, able to handle ambiguity, identify risks, mitigate, and autonomously find the right people and tools to get the job done
Proven track record of mentoring and growing engineers, lift the level of software engineering excellence in a ML data ops team typically focused on short term deliveries