Interested in this AI/ML Engineer role at Siemens?
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The AI Factory Electrical Engineer is a vital member of the AI Factory program, specializing in electrical schematics modeling and technical illustration. This role is crucial for supporting the detailed electrical design of our cutting\-edge AI infrastructure, collaborating with engineering experts across various voltage levels. You will be a key contributor to our technical design workflow, helping to create and maintain precise electrical models and illustrations that underpin the reliability and performance of our AI Factory solutions.
You’ll make an impact by
- Supporting electrical modeling, creating detailed schematics, and developing technical illustrations for the AI Factory program.
- Becoming a key contributor to the technical design workflow, ensuring accuracy and clarity in all electrical documentation.
- Taking responsibility for the detailed electrical schematics of the AI Factory program, working closely with engineering experts in high voltage, medium voltage, and low voltage systems.
- Applying working knowledge of Siemens tools to develop reference designs and standardized electrical solutions.
- Assisting in power system studies and modeling, including EMT studies, short circuit analysis, and protection coordination, utilizing industry software.
- Contributing to the construction and maintenance of models for electrical equipment, devices, and systems.
- Collaborating effectively within fast\-paced, multi\-disciplinary teams to achieve project goals and deliver robust electrical designs.
Core Competencies
- Electrical Schematics \& Modeling: Foundational knowledge and practical experience in creating detailed electrical schematics and technical illustrations.
- Power Systems Analysis: Familiarity with power system studies, including EMT studies, short circuit analysis, and protection coordination.
- Electrical Equipment Modeling: Ability to construct and maintain models of electrical equipment, devices, and systems.
- Industry Software: Working knowledge of industry software for power system studies (e.g., PSS®E, PSS®SINCAL, PSCAD).
- Data Center Power: Familiarity with data center power system topology and protection concepts.
- Siemens Tools: Working knowledge of Siemens tools relevant to electrical design and modeling.
- Team Collaboration: Demonstrated ability to perform effectively within fast\-paced, multi\-disciplinary teams.
Basic Qualifications:
- Master’s degree in Electrical Engineering or a related technical field.
- 1\+ years of experience in electrical schematics/SLD creation.
- Familiarity with power system studies and modeling concepts, including but not limited to short\-circuit studies, protection coordination studies, RMS stability, and EMT simulation.
- Experience in constructing and maintaining models of electrical equipment.
- Ability to work effectively in fast\-paced, multi\-disciplinary environments.
- Legally authorized to work in the United States on a continual and permanent basis without company sponsorship.
Preferred Qualifications:
- PhD in Electrical Engineering with a focus on high\-voltage DC systems, advanced power electronics, or complex electrical grid simulations.
- Demonstrated research experience in novel electrical system designs, particularly with high\-voltage direct current (HVDC) applications.
- Demonstrated experience with power system studies using software such as PSS®E, PSS®SINCAL, and/or PSCAD.
- Demonstrated experience with data center power system topology and protection concepts.
- Prior experience in a role supporting data center power distribution system designs or standardized electrical solutions.
- Ability to collaborate with high, medium, and low voltage engineering and mechanical engineering experts.
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.
You’ll Benefit From
Siemens offers a variety of health and wellness benefits to our employees. Details regarding our benefits can be found here: https://www.benefitsquickstart.com/siemens/index.html
The pay range for this position is $102,459 \- $210,773 annually with a target incentive of 10% annually. Actual offer may be lower or higher depending on selected professional.
Salary Context
This $102K-$210K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($156K) sits 27% below the category median. Disclosed range: $102K to $210K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Siemens AI Hiring
Siemens has 3 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer. Positions span Santa Clara, CA, US, Princeton, NJ, US, Raleigh, NC, US. Compensation range: $210K - $216K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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