Lead AI Engineer

OVERVIEW

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.

NOTICE OF JOB OPPORTUNITY

COMPANY:

Ulta, Inc.

LOCATION:

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

TITLE:

Lead Artificial Intelligence Engineer

HOURS:

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

DUTIES

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 team, fostering a culture of technical excellence. 10.0%

Communication: Present technical results and architectural decisions to senior stakeholders, effectively communicating the trade-offs between model complexity, performance, and cost.

Serve as an escalation point and technical authority for the team. 10.0%

REQUIREMENTS:

Bachelor’s degree in Computer Science, Mathematics, Physics, Statistics or other quantitative field.

Seven (7) years in any occupation with experience working in data science, machine learning or AI.

Seven (7) years in any occupation with experience working in data science, machine learning or AI must include: Seven (7) years of experience writing production-quality object-oriented code (Python or C++) and working with ML frameworks such as scikit-learn, TensorFlow, or similar, including five (5) years of experience building low-latency or real-time ML systems; Seven (7) years of experience designing, building, and deploying large-scale recommender or decisioning systems, including techniques such as ranking, clustering, and other unsupervised learning methods; Seven (7) years of experience building and deploying production ML systems on cloud platforms (Google Cloud, Azure, or AWS); Six (6) years of experience applying optimization techniques in production environments, including large-scale ML or decisioning systems such as operational research, offer optimization, or revenue maximization; Five (5) years of experience in the Retail Industry; Five (5) years of experience building scalable, production-grade real-time inference services using Docker, with CI/CD and monitoring; Three (3) years of experience as Sr AI or ML Engineer with demonstrated ability to drive technical direction and own AI tech solutions; Three (3) years of experience designing and developing reinforcement learning models in real-world or production settings; Two (2) years of experience working with large language models (LLMs); Two (2) years of experience building or integrating large-scale batch ML pipelines using cloud-native event-driven systems (e.g., Pub/Sub) and containerized execution (e.g., GKE); Two (2) years of experience developing agentic solutions within Databricks and Google Gemini Enterprise leveraging ADK, LangGraph – including multi-agent orchestration patterns; Two (2) years of experience partnering with AI Architects and Product team to translate requirements into agentic design patterns, defining orchestration and automation agentic behavior. Experience must also include: Experience working with large-scale data processing and analytics platforms, including Apache Spark and Databricks, for feature engineering, model training, and data pipelines; Experience with relational databases including Postgres, Bigquery and MySQL; Document databases including MongoDB; and low-latency databases including BigTable; and Experience building and delivering complex ML solutions from inception to launch.

SALARY

$214,365 - $215,365 per year

Ulta offers a standard benefits package. See a full list of benefits:

The pay range for this position is $214,365.00 - $215,365.00 / Year with the opportunity for eligible associates to earn additional compensation pursuant to the Company’s bonus plan. Exact pay will be based on factors including, but not limited to relevant education, qualifications, certifications, experience, level, shift, geographic location, and business and organizational needs. Full-time positions are eligible for paid time off, health, dental, vision, life and disability benefits. Part-time positions are eligible for dental, vision, life, and disability benefits. For additional information concerning our benefits, visit our Benefits and Career Development page:

ABOUT

AtUlta Beauty(NASDAQ: ULTA),the possibilities are beautiful. Ulta Beauty is the largest North American beauty retailer and the premier beauty destination for cosmetics, fragrance, skin care products, hair care products and salon services. We bring possibilities to life through the power of beauty each and every day in our stores and online with more than 25,000 products from approximately 500 well-established and emerging beauty brands across all categories and price points, including Ulta Beauty’s own private label. Ulta Beauty also offers a full-service salon in every store featuring—hair, skin, brow, and make-up services.

We will consider for employment all qualified applicants, including those with arrest records, conviction records, or other criminal histories, in a manner consistent with the requirements of any applicable state and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, and the New York City Fair Chance Act.