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About This Role
The UL Solutions Research and Development team and Modeling and Simulation team is seeking a Simulation Engineer with a strong CFD, fire dynamics and thermal sciences background to support fire and explosion hazard modeling. This role applies advanced computational fluid dynamics, heat transfer, and combustion modeling to evaluate battery fires, gas explosions, and performance\-based fire safety designs.
The engineer will primarily work with Flame Acceleration Simulator (FLACS) for deflagration explosion and gas dispersion analysis and Fire Dynamics Simulator (FDS), fire dynamics, smoke, and thermal impact modeling. The position supports large\-scale battery fire testing, performance\-based design (PBD), code compliance, and customer\-facing engineering projects within the energy and built\-environment sectors.
This role is intended for engineers trained in thermal\-fluid sciences or CFD, who wish to specialize in fire and explosion safety engineering.
- Develop and execute CFD\-based fire and explosion simulations using:
+ FLACS for gas dispersion, flame acceleration, and vented/unvented deflagration analysis
+ FDS for gas dispersion, fire dynamics, smoke movement, heat transfer, and plume behavior
- Model ESS/BESS fire and explosion scenarios, including:
+ Battery off\-gas release and dispersion
+ Ignition and overpressure development
+ Large\-scale enclosure and outdoor fire scenarios
+ Apply strong fundamentals in heat transfer (convection, conduction, radiation), fluid flow and turbulence, combustion, and fire dynamics
- Analyze simulation outputs such as:
+ Temperature fields, heat fluxes, and thermal exposure
+ Overpressure and impulse
+ Gas concentration and visibility
- Support performance\-based fire and explosion safety design (PBD) aligned with applicable codes and standards
- Interpret simulation results in the context of code intent, safety objectives, and risk reduction strategies
- Assist in defining credible worst\-case and design fire/explosion scenarios
- Perform pre\-processing, solver execution, and post\-processing of CFD simulations
- Support verification and validation (V\&V) through comparison with:
+ Large\-scale ESS fire test data
+ Literature and experimental benchmarks
- Prepare clear, defensible technical reports for internal review and external customers
- Work under guidance of senior fire and explosion engineers while progressively taking ownership of tasks
- Collaborate with multidisciplinary teams across CFD, testing, certification, and research
- Maintain high\-quality documentation and adherence to established modeling workflows
- Actively develop expertise by reviewing technical literature and internal best practices
- Bachelor’s or Master’s degree in Mechanical Engineering / Chemical Engineering/ Thermal Sciences/ Combustion, Fire Protection Engineering, or CFD\-related disciplines
- 3–5 years of experience in CFD, thermal\-fluid simulation, fire modeling, or related applied simulation work
- Hands\-on experience or project exposure to:
+ Fire Dynamics Simulator (FDS) and
+ FLACS or similar explosion / gas dispersion tools
+ Computer Aided Design (CAD) software (e.g. Inventor, SolidWorks)
- Ability to apply engineering judgment and first principles when building simulation models
- Excellent technical writing and verbal communication skills
- Comfortable working independently on defined tasks within a structured engineering framework
- Exposure to battery energy storage systems (BESS/ESS) or lithium\-ion thermal runaway (nice to have)
- Familiarity with large\-scale fire testing or fire test data interpretation (nice to have)
- Awareness of performance\-based fire engineering concepts (nice to have)
- Basic knowledge of relevant standards (e.g., NFPA 855, NFPA 68/69, IFC, international fire codes) (nice to have)
- Experience with other CFD tools (ANSYS Fluent, OpenFOAM, CFX)
- Experience with Python workflow automation (nice to have)
- Interest in research, model validation, and advanced simulation methodologies
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 $100,000 to $130,000 USD 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 10% 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).
\#LI\-SG2
\#LI\-Hybrid
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 $100K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($115K) sits 47% below the category median. Disclosed range: $100K to $130K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
UL Solutions AI Hiring
UL Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Northbrook, IL, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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