Principal Applied Scientist

$200K - $271K New York, NY, US Senior Research Scientist

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Skills & Technologies

PythonRag

About This Role

AI job market dashboard showing open roles by category

Siemens builds the systems the physical world runs on: factories, power grids, buildings, trains, hospitals. Industrial and physical AI is a major opportunity in applied AI, and one of the harder ones to get right. There is a generation of AI-powered products to build.

We are an applied science organization building the science behind them. The work sits at the intersection of machine learning research, real world data, and production systems running in industrial environments.

As Principal Applied Scientist, you lead the science on a major capability area inside the pod. You take ambiguous problems through hypothesis, method selection, experimentation, and into models that run in production. You are hands on with the modeling, and you are accountable for the rigor and the outcome.

This is a senior individual contributor role. You will work in close partnership with engineers and product managers who turn your work into product.

Key responsibilities

  • Own one or more scientific capability areas end to end, for example perception, computer vision, language and agents, time series, control, planning, or evaluation
  • Take problems from ambiguous product or system requirements through clear research questions, hypotheses, and success metrics
  • Lead applied research projects: literature review, method selection, experimentation, ablation, error analysis, and productization
  • Build and run the evaluation pipelines for the work you own: offline metrics, online experiments, robustness testing in industrial conditions
  • Work with engineers to take models into production grade pipelines: data readiness, training infrastructure, inference, observability
  • Make scientific tradeoffs in front of engineers and product managers, with evidence, and translate them into decisions the team can act on
  • Identify and de-risk scaling challenges in your area: data quality, model drift, latency, throughput, cost, safety
  • Raise the bar on experimentation rigor, reproducibility, and documentation across the team
  • Apply responsible AI practices in your work: bias detection, model risk management, human in the loop controls

Basic qualifications

  • 8+ years in applied machine learning, AI research, or data science, with models that shipped to production and made an impact
  • Strong foundation in machine learning theory and practice across training, evaluation, and deployment
  • Demonstrated experience taking research from a paper or prototype into a model that runs reliably in production
  • Proficiency in Python and modern ML frameworks and toolchains
  • Strong partnership track record with engineering teams on data, training infrastructure, and inference
  • Clear written and verbal communication with engineers, product managers, and senior leaders

Preferred qualifications

  • Experience applying ML in industrial or physical domains: manufacturing, automation, robotics, energy, mobility, infrastructure, healthcare
  • Deep expertise in one of: multimodal ML, generative AI, retrieval augmented generation, agentic workflows, time series, control, or planning
  • Scientific ML for physical systems: surrogate modeling, operator learning, physics-informed ML, geometry-aware ML, differentiable simulation, AI for semiconductor/EDA
  • Hands-on experience building evaluation pipelines, running online experiments, or instrumenting production monitoring for a model you owned
  • Publications, patents, open source contributions, or significant internal technology transfers
  • Experience mentoring more junior scientists and engineers
  • Experience working with globally distributed research, product, or engineering organizations

About the Team:

We are an early-stage engineering team solving hard technical problems at the intersection of AI, systems, and real-world interaction. We operate with high autonomy, collaborate closely across functions, and maintain high technical standards. Principal Engineers at our company are expected to lead by example, model engineering excellence, and shape both what we build and how we build it.

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.

$200,614 $271,483 15%

Salary Context

This $200K-$271K range is above the 75th percentile for Research Scientist roles in our dataset (median: $199K across 63 roles with salary data).

Role Details

Company Siemens
Title Principal Applied Scientist
Location New York, NY, US
Category Research Scientist
Experience Senior
Salary $200K - $271K
Remote No

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 9,079 AI roles we're tracking, Research Scientist positions make up 2% 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

Python (18% of roles) Rag (7% of roles)

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 749 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($236K) sits 6% above the category median. Disclosed range: $200K to $271K.

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

AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national median.

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 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).

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 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

Based on 749 roles with disclosed compensation, the median salary for Research Scientist positions is $222,200. Actual compensation varies by seniority, location, and company stage.
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.
About 6% of the 9,079 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Siemens is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from Research Scientist positions include Research Lead, Distinguished Scientist, VP of Research. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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