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About This Role
Research Scientist, Physics x AI
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!
In this role you will work onsite in our Princeton, NJ office.
We are an AI research Lab specializing in Physics x AI research. Our mission is to scale the capabilities of AI in the physical domain, encoding diverse scientific and engineering knowledge into neural networks to tackle complex, high\-impact industrial and scientific problems. As an integral part of Siemens’ Data Analytics \& AI organization, we drive physical AI innovation for an industrial scale.
Our team is part of Siemens Technology, Siemens’ central in\-house research department. It acts as a strategic partner to support our executive units with R\&D and innovation services, protects the company's intellectual property, coordinates collaborations with top universities worldwide, and supports standardization activities in all business units and regions.
As a Physics x AI Research Scientist, you will explore and build the next generation of scalable, generalizable physics\-aware foundation models. We actively welcome candidates from a broad range of backgrounds—including fluid dynamics, electromagnetics, electrical engineering, systems engineering, and biological domains such as protein folding—who are passionate about encoding physical, biological, or engineering priors into modern AI systems. You’ll focus on architectures (especially transformers) that move beyond the limitations of narrow, hard\-coded models into robust solutions that generalize across geometries, conditions, and disciplines.
Your future impact areas may include:
- Systems engineering and verification workflows (from design through manufacturing)
- AI\-native electronic design automation (EDA) and chip design
- Any other discipline where physical or biological priors in AI can bring breakthrough scale and generalization
A research scientist in our team will:
- Translate business needs into actionable project proposal, negotiate project terms and budget
- Steer delivery of complex, multi\-national, strategic projects in cooperation with stakeholders and the project team
- Validate the market potential of new AI innovation and develop business models for products, services, and solutions.
- Drive ideas from ideation to productization with a customer mindset
- Own global Siemens cross\-company technology roadmaps and shape the interfaces to other technologies
- Work with our U.S.\-based and global research groups to proactively shape the strategy of our AI research and pre\-development operation in the US.
- Shape the subsidies and grants portfolio for our AI research and pre\-development in the U.S.
- Communicate complex technology topics to non\-experts and executive decision\-maker
- Conduct hands\-on research and development on scalable, physics\-aware foundation models
- Model and encode knowledge from diverse scientific and engineering domains \- including but not limited to fluid dynamics, electromagnetics, electrical engineering, biology/protein folding (excluding quantum science)
- Document and publish research, and present proposals and results confidently to both internal and external large audiences
- Proactively engage with Siemens domain experts, their customers and partners to understand real\-world problems, collect requirements, and co\-create innovative solutions
- Translate cutting\-edge research into industrial impact, including proof\-of\-concept projects, identifying and closing research gaps
- Identify, adopt, and apply evolving trends in machine learning, physics\-informed AI, and AI for science
- Collaborate with our interdisciplinary Princeton\-based lab and contribute to Siemens’ global Data Analytics \& AI network
Basic Qualifications:
- Master's degree with relevant research/industry experience or a PhD degree in a STEM field, i.e. Computer Science, Physics, Applied Mathematics, Electrical Engineering, Mechanical Engineering, Biophysics, Bioinformatics, or similar.
- Established track record of publication or patent applications
- Proficiency in one or more modern programming languages, i.e. Python, C\+\+
- Experience with one or more of the following AI programming libraries: PyTorch, JAX, NVIDIA NeMO, HF Transformers
- Proficiency in English both written and verbal
- Authorized to work in the United States without Corporate Sponsorship now or in the future (current Siemens employees on visa will be considered)
- Successful candidates must be able to work with controlled technology in accordance with U.S. Export Control Law. U.S. Export Control laws and applicable regulations govern the distribution of strategically important technology, services and information to foreign nationals and foreign countries. Siemens may require candidates under consideration for employment opportunities to submit information regarding citizenship status to allow the organization to comply with specific U.S. Export Control laws and regulations.
Preferred Qualifications:
- Proven ability to translate ideas from state\-of\-the\-art research into real\-world/business impact
- Demonstrated research background with strong publication record (e.g. NeurIPS, ICML, IEEE, AAAI, L4DC) or equivalent industrial research achievements in industrial AI, physics\-informed AI, physical AI and/or AI for science
- Demonstrated ability to training neural architectures capable of learning and/or encoding priors from physics, chemistry, biology and/or engineering
- Experience with large\-scale, distributed AI/foundation model training
- Fundamental knowledge of software engineering principles and modern software development methodologies
- Eagerness and ability to present research proposals and results confidently to large, diverse technical and business audiences
- Familiarity with AI\-based surrogate modelling for CFD, AI\-native chip design, protein folding, or verification workflows
- Domain Knowledge and/or experience in the fields of Predictive Maintenance for Industrial Infrastructure, Automation Engineering, Manufacturing, or Electrification are a plus
- Experience participating in international project teams and strong customer orientation
- Proven track record of successfully responding to U.S. government grants is a plus
- Strong knowledge of the Siemens product portfolio is a plus
- Additional language skills, in particular German, are a plus.
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 Diversity, Equity, and Inclusion:
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. Come bring your authentic self and create a better tomorrow 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.
\#AIforScience \#IndustrialAI \#MachinelearningResearch \#PhysicsinformedAI
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$126,307 $216,526 10%
Salary Context
This $126K-$216K range is below the median for Research Scientist roles in our dataset (median: $183K across 83 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 3,708 AI roles we're tracking, Research Scientist positions make up 3% of the market. At Siemens, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $222,200 based on 197 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($171K) sits 23% below the category median. Disclosed range: $126K to $216K.
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 Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
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).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
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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