SWE – Machine Learning Technical Lead, Data Engineering Full-time

Summary
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.

In this position, you will:

– Collaborate with a variety of partners, from infrastructure, ML research teams to our data functions, including data engineers, to assess the needs

– Identify state of the art data components used to store, expose and track ML data

– Identify opportunities for improvement of internal infrastructure offerings, and influence roadmaps of partner teams to build or improve key components we rely on

– Design and execute the roadmap for adoption of new components, build the pipelines necessary to connect data systems and teams

– Improve automation workflows, data visualization tools, ML enrichment, asset lineage tracking, including data coming from sophisticated synthetic workflows, data governance and compliance components, storage and tracking of synthetic data

– Be hands on, actively participate to the stack and implement high quality code