Interested in this AI/ML Engineer role at Siemens?
Apply Now →About This Role
Industrial AI Strategist
Here at Siemens, we take pride in enabling sustainable progress through technology. We do this through empowering customers by combining the real and digital worlds. Improving how we live, work, and move today and for the next generation! We know that the only way a business thrives is if our people are thriving. That’s why we always put our people first. Our global, diverse team would be happy to support you and challenge you to grow in new ways. Who knows where our shared journey will take you?
Transform the everyday with us!
===================================
You have spent years building at the intersection of AI and industry. You know how to talk to engineers and executives alike. You have a network that opens doors and the technical depth to walk through them. You have been waiting for a role that values both sides of who you are. This is that role.
We are looking for an Industrial AI Strategist. This position will be based in Princeton, New Jersey, with regular presence at our New York City office.
Location and Travel
=======================
In this role you will work between 2 locations.This role is primarily based in Princeton, New Jersey, with regular presence at our New York City office. You will work across both locations as part of our day\-to\-day rhythm, with approximately 20% national and international travel for customer engagements, conferences, and partner meetings.
The Opportunity
===================
Siemens is one of the world's leading technology companies, with a presence in 190\+ countries and deep expertise across industrial sectors, from manufacturing and mobility to infrastructure and beyond.
The Industrial AI Lab is part of Siemens' Data and AI organization, operating at the intersection of customers, cutting\-edge AI research, and industrial domains. Our mission is clear: discover and validate the next scalable industrial AI product ideas, catalyze existing product development teams, drive Industrial AI thought leadership activities, and build a thriving Data and AI community within Siemens.
We are a small, high\-impact team and we move fast. Through close collaboration with customers, academia, and industry partners, we bring together internal expertise, external partnerships, and advanced AI capabilities to tackle real\-world industrial challenges that matter. Our reach spans the U.S. industrial ecosystem and extends globally, and we are growing.
You’ll make an impact by:
=============================
- Designing and facilitating customer\-facing AI workshops and ideation sessions that uncover high\-value opportunities for industrial AI.
- Evaluating emerging ideas with technical rigor and helping prioritize the concepts most likely to create meaningful, scalable impact.
- Serving as the bridge between business stakeholders and technical teams by validating concepts, connecting the right people, and ensuring delivery teams have the context, partnerships, and clarity needed to move forward.
- Helping embed AI innovation into customer engagements, industry showcases, and U.S. market narratives.
- Co\-developing thought leadership content and events that translate complex technical capabilities into tangible customer value alongside business and sales teams.
- Shaping the Lab's external presence by engaging with academic and industry partners, representing Siemens at conferences and forums, and helping define the conversation around Industrial AI across the U.S. market.
You’ll win us over by having the following qualifications:
==============================================================
Basic Qualifications:
-------------------------
- Bachelor’s degree in a STEM field, such as computer science, mathematics, physics, or engineering.
- 5\+ years of work experience in a relevant role in the AI, machine learning, or innovation space, such as AI\-focused consulting, industrial technology, applied AI, or a combination.
- Demonstrated track record of working across technical and business stakeholders.
- Proficiency in English, both written and verbal.
- Legally authorized to work in the United States without corporate sponsorship now or in the future. Current Siemens employees on visa will be considered.
- Ability to work with controlled technology in accordance with U.S. Export Control Law. Siemens may require candidates under consideration to submit information regarding citizenship status to allow the organization to comply with specific U.S. Export Control laws and regulations.
Preferred Qualifications:
-----------------------------
- MBA or master’s degree in computer science, mathematics, physics, engineering, or a related technical field, paired with a strong academic record.
- Experience building AI solutions, ideally in industrial or operational environments, with the ability to evaluate ideas critically and engage credibly with data scientists, machine learning engineers, and AI researchers.
- Understanding of how AI applies in industrial environments, including manufacturing, automation, infrastructure, mobility, or adjacent verticals.
- Entrepreneurial mindset with a hands\-on approach, proactive ownership, and the ability to shape opportunities in ambiguous environments.
- Advanced communication skills with the ability to present to C\-suite stakeholders and technical engineering audiences with equal credibility.
- Established network across the U.S. industrial and AI ecosystem, including customers, startups, academic institutions, investors, or enterprises.
- Ability to design workshops, activate partnerships, make meaningful introductions, and turn conversations into collaboration.
- Familiarity with Siemens' technology portfolio or industrial software solutions.
- Experience engaging with innovation programs, accelerators, or research institutions.
Why this role
=================
- Global Scale, Local Impact: A well\-connected platform that allows you to create global impact.
- Intellectual Depth: Real AI thought leadership, academic partnerships, and industry influence.
- High Visibility: A small team with start\-up spirit where your contributions are seen and felt.
- Both Sides of You, Valued: Your people skills and technical depth matter equally here.
- A Team Worth Joining: Fast\-moving, curious, and working on some of the most exciting challenges in AI today.
Ready to shape the future of Industrial AI?
===============================================
If you have been building at the intersection of AI and industry and you are ready for a role where your network and technical depth finally come together, we want to hear from you.
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.
Protecting the environment, conserving our natural resources, fostering the health and performance of our people as well as safeguarding their working conditions are core to our social and business commitment at Siemens. They are an integral part of our Business Conduct Guidelines and our corporate strategy.
\#LI\-JS
\#LI\-onsite
$92,320 $158,263 10%
Salary Context
This $92K-$158K 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 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 $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 ($125K) sits 43% below the category median. Disclosed range: $92K to $158K.
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.
Siemens AI Hiring
Siemens has 4 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span New York, NY, US, Santa Clara, CA, US, Seattle, WA, US. Compensation range: $158K - $341K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.