Principal Engineer - AI​/ML Platform; Remote Or Hybrid

The pay range is $ - $. 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

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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  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.

This role is ideal for a technology leader who enjoys solving complex platform challenges, influencing engineering strategy, and building capabilities that enable hundreds of engineers and data scientists to deliver AI solutions efficiently and safely.

• Define the long-term technical strategy and architecture for the enterprise ML Operations Platform.
• Design scalable, secure, and resilient cloud-native platforms supporting machine learning workloads.
• Establish best practices for model development, deployment, monitoring, and lifecycle management.
• Lead architecture for enterprise machine learning infrastructure supporting batch, streaming, and real-time inference.
• Drive adoption of cloud-native technologies, Kubernetes, and modern platform engineering practices.
• Define standards for model governance, observability, reliability, explainability, and responsible AI.
• Partner with infrastructure, security, and engineering teams to improve platform scalability, performance, and operational efficiency.
• Evaluate emerging technologies and recommend architectural approaches that improve platform capabilities.
• Mentor engineers and influence technical direction across multiple engineering organizations.

Core responsibilities of this job are articulated within this job description. Job duties may change at any time due to business needs.

• MS in Computer Science, Engineering, Mathematics, or related technical field with relevant software engineering experience
• Extensive experience designing and delivering large-scale cloud-native platforms or distributed systems
• Deep experience building and operating enterprise machine learning platforms and MLOps capabilities
• Strong understanding of machine learning lifecycle management, deployment strategies, observability and production operations
• Demonstrated experience with machine learning platforms and tooling such as Vertex AI, Kubeflow, MLflow, and/or equivalent technologies
• Experience building developer platforms or internal platform products
• Experience with distributed training, GPU infrastructure, and large-scale inference platforms
• Experience with feature management, model governance, and responsible AI practices.
• Familiarity with Generative AI platforms and infrastructure supporting…