Interested in this AI/ML Engineer role at UL Solutions?
Apply Now →About This Role
This role is a hybrid 3 days a week on-site position, based either at the Chicago, IL or NBK office. With ability to travel to NBK or Chicago as needed.
The Executive Assistant is a dedicated support to the EVP, Chief AI & Digital Trust Officer. This role serves as a trusted partner to the executive, managing complex priorities, coordinating a demanding calendar and travel schedule, supporting key strategic initiatives, and facilitating effective communication across senior leadership and internal and external stakeholders.
The ideal candidate is highly organized, proactive, comfortable operating in a fast-paced global environment, and able to exercise sound judgment and discretion when handling sensitive and confidential information.
- Provide high-level executive and administrative support to the EVP, Chief AI & Digital Trust Officer.
- Manage a complex executive calendar, proactively prioritizing meetings, commitments, and competing business needs.
- Coordinate domestic and international travel, including complex itineraries and associated logistics.
- Prepare and coordinate materials for executive meetings, including agendas, presentations, reports, briefing materials, and follow-up items.
- Support high-level, confidential strategic projects and initiatives.
- Serve as a key administrative liaison between the executive and senior leaders, internal teams, and external stakeholders.
- Coordinate leadership meetings, global virtual Town Halls, knowledge-sharing sessions, and other key organizational events.
- Assist with preparation of reports, presentations, data, charts, and other executive-level materials.
- Track priorities and follow-up items to help ensure commitments and key initiatives remain on schedule.
- Coordinate communications and information flow across the function.
- Create and distribute functional updates and maintain relevant SharePoint resources.
- Support onboarding and offboarding activities across the function.
- Coordinate employee engagement activities and other department initiatives.
- Support administrative aspects of the function's budget and operational activities.
- Handle sensitive and confidential information with a high degree of professionalism and discretion.
- Anticipate executive needs, identify potential scheduling or operational conflicts, and independently resolve issues when appropriate.
- Higher Secondary Education required; additional education preferred.
- 7+ years of experience as an Executive Assistant
- Demonstrated experience supporting senior executive leadership.
- Strong experience managing complex executive calendars, meetings, and domestic/international travel.
- Excellent organizational skills and ability to manage multiple competing priorities.
- Exceptional written and verbal communication skills.
- Strong judgment, professionalism, discretion, and ability to work with confidential information.
- Ability to work effectively with senior leaders and build relationships across a global organization.
- Demonstrated ability to work independently, anticipate needs, solve problems, and make sound decisions.
- Strong proficiency with Microsoft 365 tools, including Outlook, Teams, PowerPoint, Word, Excel, and SharePoint.
- Experience supporting technology, AI, digital transformation, cybersecurity, digital trust, or other highly technical executive functions is preferred.
What you’ll experience working for ULS
UL Solutions has been pioneering change since 1894 and we’re still leading the way. From day one, we’ve blazed a trail protecting the planet and everyone on it. Our teams have influenced billions of products, plus services, software offerings and more. We break things, burn things and blow things up. All in the name of safety science.
That’s where you come in — because none of it could happen without you. It takes passion to protect people, problem-solving to safeguard personal data and conviction to make the world a more sustainable place. It takes bold ideas and brilliant minds to build a better world for future generations across the globe.
This is more than a job. It’s a calling. A passion to use our expertise and play our part in creating a more secure, sustainable world today — and tomorrow. As a member of our safety science community, you’ll use your ideas, your energy and your ambition to innovate, challenge and ultimately, help create a safer world.
Everyone here is unique. But we’re also a global community, working together to help create a safer world. Join UL Solutions and you can connect with the brightest minds in the business, all bringing their distinct perspectives and diverse backgrounds together to deliver real change.
Empowering our customers to keep the world safe means thinking ahead. It means investing in training and empowering our people to learn and innovate. At UL Solutions, we help build a better future — one where everyone benefits.
Join UL Solutions to be at the center of safety. To learn more about us and the work we do, visit UL.com
Total Rewards: We understand compensation is an important factor as you consider the next step in your career. The estimated salary range for this position is $80,000 to $100,000 and is based on multiple factors, including job-related knowledge/skills, experience, geographical location, as well as other factors. This position is eligible for annual bonus compensation with a target payout of 5% of the base salary. This position also provides health benefits such as medical, dental and vision; wellness benefits such as mental and financial health; and retirement savings (401K) commensurate with the standard rewards offered in each individual location or country. We also provide full-time employees with paid time off including vacation (15 days), holiday and personal days (totaling 12 days) and sick time off (72 hours).
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A global leader in applied safety science, UL Solutions (NYSE: ULS) transforms safety, security and sustainability challenges into opportunities for customers in more than 110 countries. UL Solutions delivers testing, inspection and certification services, together with software products and advisory offerings, that support our customers’ product innovation and business growth. The UL Mark serves as a recognized symbol of trust in our customers’ products and reflects an unwavering commitment to advancing our safety mission. We help our customers innovate, launch new products and services, navigate global markets and complex supply chains, and grow sustainably and responsibly into the future. Our science is your advantage.
Salary Context
This $80K-$100K range is in the lower quartile 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 UL Solutions, 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 in Demand for This Role
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. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($90K) sits 58% below the category median. Disclosed range: $80K to $100K.
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.
UL Solutions AI Hiring
UL Solutions has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Chicago, IL, US, Melville, NY. Compensation range: $100K - $100K.
Location Context
AI roles in Chicago pay a median of $193,440 across 450 tracked positions. That's 9% below the national 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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