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About This Role
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 working 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
APPLY: https://careers.ulta.com/careers/544938 Ulta offers a standard benefits package. See a full list of benefits: https://careers.ulta.com/careers/about-us/benefits-and-career-development : 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: https://learn.bswift.com/ulta About:
At Ulta 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.
Salary Context
This $214K-$215K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Ulta, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $214K to $215K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Ulta AI Hiring
Ulta has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bolingbrook, IL, US. Compensation range: $215K - $215K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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