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The AIF Liquid Cooling Expert is a Senior Technical contributor for Siemens AI Factory program, responsible for defining and advancing compute-side
liquid cooling architectures across AI Factory and data center environments. This role integrates physical and digital cooling technologies and collaborates
closely with multi-disciplinary teams to deliver scalable, reference-ready cooling solutions for Siemens portfolios and customers.
You’ll make an impact by:
- Leading the compute-side cooling strategy for the AI Factory (AIF) program, collaborating with business and technical experts across controls,
simulation, air handling, power, and compute.
- Designing and integrating both physical cooling infrastructure (primary and secondary loops, cold plates, manifolds, valves, pumps, CDUs) and
digital simulations, including hydraulic system simulation, thermal/ CFD simulation.
- Co-creating with peers across disciplines to optimize Siemens reference architectures and customer-specific AI Factory and data center designs.
- Creating next-generation cooling concepts u sing simulation tools such as Amesim, Modelica, STAR-CCM+, leveraging automation and controls
through PLCs.
- Developing scalable system designs, reusable libraries, and automation-ready assets deployed across Siemens portfolios and customer solutions, including AI-assisted design workflows.
- Incorporating emerging code and standards from OCP, ASHRAE, UL, etc. into forward-looking cooling designs while operating in fast-paced, multi-disciplinary environments.
- Reducing complex cooling systems into reference designs and foundational guidelines enabling consistent execution across portfolio, advanced development, and R&D teams.
Core Competencies:
- Liquid Cooling Systems Expertise: Deep expertise in direct-to-chip and immersion cooling technologies and cold plate, CDU
architectures. Familiarity with hydraulic and CFD simulations.
- Cooling System Architect: Leads physical and digital liquid cooling designs for AI Factory and data center environments, considering
hydraulic and thermal performance, reliability, and efficiency.
- Scalable Design Strategy: Translates complex cooling systems into reusable, reference-ready architectures.
- Standards-Driven Design: Integrates emerging code and standards from OCP, ASHRAE, UL, etc. into future-ready solutions.
- Collaboration & Influence: Operates effectively across fast-paced, multi-disciplinary teams.
Basic Qualifications:
- Bachelor’s degree in Mechanical Engineering, Thermal Engineering, or related field.
- 8+ years of experience in liquid cooling, thermal systems, or data center cooling design.
- Hands-on experience with end-to-end compute-side cooling system design, hydraulics and thermal system modeling.
- In-depth knowledge of control and monitoring requirements for data center cooling systems
- Ability to travel domestically up to 90% in the first 6 months and internationally as required.
- Legally authorized to work in the United States on a continual and permanent basis without company sponsorship
Preferred Qualifications:
- Advanced degree (Master’s or PhD) in thermal, mechanical, or related engineering discipline.
- Experience designing liquid cooling systems for AI, HPC, or large-scale data center environments, both mechanical and controls.
- Prior experience in modular construction methods for mechanical and/or cooling design in data centers
- Hands-on experience with Siemens modeling, PLM, or automation platforms.
Ready to create your own journey? Join us today!
About Siemens
We are a global technology company focused on industry, infrastructure, transport, and healthcare. From more resource-efficient factories, resilient
supply chains, and smarter buildings and grids, to sustainable transportation as well as advanced healthcare, we create technology with purpose adding
real value for customers.
Our Commitment to Equity and Inclusion in our Diverse Global Workforce
We value your unique identity and perspective. We are fully committed to providing equitable opportunities and building a workplace that reflects the
diversity of society, while ensuring that we attract the best talent based on qualifications, skills, and experiences. We welcome you to bring your authentic
self and transform the everyday with us.
$146,000 $198,000 15
Salary Context
This $146K-$198K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Siemens, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($172K) sits 19% below the category median. Disclosed range: $146K to $198K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Siemens AI Hiring
Siemens has 9 open AI roles right now. They're hiring across AI/ML Engineer, Prompt Engineer, Research Scientist, Data Scientist. Positions span Buffalo Grove, IL, US, Ostrava, Moravia-Silesia, Czech Republic, New York, NY, US. Compensation range: $144K - $271K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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