Target

Principal Engineer - AI Platform (AI Factory /Advance AI)(Remote Or Hybrid)

Brooklyn Park, MN, US$168,000-$303,000Posted 14 days ago

Job Description

The pay range is $168,000.00 - $303,000.00 Pay is based on several factors which vary based on position. These include labor markets and in some instances may include education, work experience and certifications. In addition to your pay, Target cares about and invests in you as a team member, so that you can take care of yourself and your family. Target offers eligible team members and their dependents comprehensive health benefits and programs, which may include medical, vision, dental, life insurance and more, to help you and your family take care of your whole selves. Other benefits for eligible team members include 401(k), employee discount, short term disability, long term disability, paid sick leave, paid national holidays, and paid vacation. Find competitive benefits from financial and education to well-being and beyond at https://corporate.target.com/careers/benefits.

PRINCIPAL ENGINEER – AI PLATFORMS

About Us

Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here.

Target's AI Platform organization is building the next generation of enterprise AI capabilities that enable teams to develop, deploy, govern, and operate Machine Learning and Generative AI solutions at scale. Our platform powers AI innovation across the enterprise by providing secure, scalable, and reusable capabilities that accelerate development while maintaining enterprise standards for reliability, governance, and operational excellence.

As a Principal Engineer – ML Operations Platform, you will provide technical leadership in defining the architecture and evolution of our enterprise machine learning platform. You will work across engineering, data science, infrastructure, security, and product organizations to establish scalable patterns for developing, deploying, monitoring, and governing machine learning systems throughout their lifecycle.

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