Lead AI Engineer

Live the experience. From professional empowerment to continual learning opportunities. From ongoing investment in new and emerging technologies to a career of self-determination. At Ulta Beauty, our tech team is critical to our scalability—and is recognized that way. We’ve been defined as a “mature start-up.” A place where interdepartmental exposure, open doors, and genuine collaboration is ubiquitous. Where challenges come fast and furious, requiring agility, mental dexterity, and creativity.

Where our passion for better solutions drives us and is core to who we are.

We’re engineering for the future of retail, and it’s no-holds-barred. But for those motivated by continual change and ambiguity, by superior leadership, by whip smart colleagues who will press you daily for your very best, you’ll find that virtually nothing’s impossible at Ulta Beauty.

Ulta, Inc.

1000 Remington Blvd., Suite 120, Bolingbrook, IL 60440

Lead Artificial Intelligence Engineer

Monday to Friday, 8:00 am to 5:00 pm

Direct and oversee the full lifecycle execution of complex AI initiatives, bridging the gap between exploratory data science and production engineering, including formulation and statistical modeling to deployment and scaling. 5.0%

Contribute technical leadership to the development and optimization of advanced algorithms for recommendation and decision engines, utilizing techniques such as collaborative filtering, embeddings, clustering, reinforcement learning, and constrained optimization. 5.0%

Architect and deploy GenAI solutions:
Design pipelines for Large Language Models (LLMs) including fine-tuning, RAG (Retrieval-Augmented Generation), and prompt engineering for enterprise retail use cases like analytics augmentation and decision support. Co-ownership (with other Engineering Lead peers and Engineering Practice lead) of governance and standards for AI engineering related portions of these technologies. 15.0%

Ensure "Enterprise Readiness":
Take ownership of the non-functional requirements of AI systems, explicitly optimizing models for low-latency inference, high throughput, data privacy compliance, and cloud cost efficiency. Help enforce adherence of others engineering team members to standards. 10.0%

Build production-grade pipelines:
Transition code from experimental notebooks to modular, version-controlled software packages. Implement automated training pipelines, CI/CD for ML, and robust monitoring for data drift and model degradation; establish team’s practices for this space. 10.0%

Code Quality & Standards:
Responsible for defining and enforcing engineering standards within the Enterprise AI team, including unit testing, documentation, peer code reviews, and reproducibility frameworks. 10.0%

Translate business needs into technical specs:
Partner with business and IT product teams to refine vague use cases into concrete engineering requirements, identifying technical risks early in the project planning process. Represent as a decision maker within the team for AIML engineering topics, and act as a primary point of contact for the Engineering domain within a team. 10.0%

Advise on emerging areas of technology and AI; evaluate and recommend AI technologies, frameworks, and tools to maintain cutting edge capabilities. 5.0%

Proactively look for opportunities to drive AI innovation, promote and advocate for those capabilities within Agile delivery framework. 5.0%

Project Estimation & Planning:
Own detailed technical roadmaps, providing accurate time and effort estimates for the Engineering phases of projects. 5.0%

Engineering Standards:
Promote adherence to statistical best practices and software engineering principles across the engineering…