Lead AI Engineer — Remote/Hybrid, Scalable ML Systems

Target; Brooklyn Park (MN); Hybrid; USD 132,000 - 286,000; Full time

Benefits offered by this job

Health benefits

401(k) plan

Paid time off

Employee discounts

Job summary

Target's Advanced AI team is seeking a Lead ML Engineer to design, build, deploy, and maintain AI/ML applications across core business workflows. You will work with data scientists, engineers, product partners, and security teams to translate ambiguous problems into scalable technical solutions.

You will provide hands-on leadership, shape architecture, evaluate models and tools, and ensure production-grade practices, observability, and documentation.

Qualifications

• Bachelors in quantitative disciplines or equivalent; MS preferred.
• 5+ years of hands-on software engineering experience.
• Strong AI/ML experience including LLMs and agentic systems.
• Proficiency with Python and AI/ML frameworks (PyTorch, TensorFlow).
• Experience with model APIs, prompt orchestration, and deployment considerations for AI systems.

Responsibilities

• Provide hands-on technical leadership for AI engineering initiatives.
• Contribute to architecture and design decisions for AI applications.
• Write maintainable production-quality code and establish engineering practices.
• Ensure AI applications are secure, reliable, and compliant with enterprise standards.

Skills

Python

AI/ML development

Communication

System design

Cross-functional collaboration

Education

Bachelor's in quantitative field

MS in CS/ML/AI or related

Tools

PyTorch

TensorFlow

LangChain

LlamaIndex

Semantic Kernel

Docker

Kubernetes

Job description

Target's Advanced AI team is seeking a Lead ML Engineer to design, build, deploy, and maintain AI/ML applications across core business workflows. You will work with data scientists, engineers, product partners, and security teams to translate ambiguous problems into scalable technical solutions.

You will provide hands-on leadership, shape architecture, evaluate models and tools, and ensure production-grade practices, observability, and documentation.