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About This Role
- 2254
- Norwood, Massachusetts
- Research/Approvals
- full-time
- Works from an office location
- 16 or 17
- USD $121,000.00/Yr.
- USD $198,000.00/Yr.
Job Description
Established nearly two centuries ago, FM is a leading mutual insurance company whose capital, scientific research capability and engineering expertise are solely dedicated to property risk management and the resilience of its policyholder-owners. These owners, who share the belief that the majority of property loss is preventable, represent many of the world’s largest organizations, including one of every four Fortune 500 companies. They work with FM to better understand the hazards that can impact their business continuity to make cost-effective risk management decisions, combining property loss prevention with insurance protection.
Schedule & Location
Employment type: Full-time, exempt salaried position based in office at FM’s Science and Technology Office in Norwood, MA.
Relocation assistance is available for this position.
Position Summary
This is a multi-level research and engineering opportunity, with scope and responsibilities aligned to the selected candidate's relevant experience, technical expertise, and demonstrated capabilities.
The Research Scientist – Computational Fluid Dynamics and Fluid–Structure Interaction (FSI) will lead and conduct research using CFD, coupled Multiphysics modeling, and FSI to assess how fluids and structures interact under environmental and extreme conditions. The successful candidate will develop, validate, and apply computational methods that improve understanding of loading, response, damage, failure, reliability, resilience, and property loss across multiple industries.
Examples of applications may include modeling wind and tornado loads on large buildings; wind loads on ground-mounted and rooftop photovoltaic (PV) systems and wind turbines; and wind, flood, wave, and current loads on structures and infrastructure. These applications will integrate FSI, finite element analysis, experiments, model validation, and uncertainty quantification to support structural risk and fragility modeling, natural hazard assessment, loss estimation, and practical loss-prevention solutions.
What You’ll Do
Depending on project needs, responsibilities may include:
- Lead research using computational fluid dynamics (CFD), fluid–structure interaction (FSI), and coupled multiphysics methods to evaluate fluid loading, structural response, heat transfer, vibration, damage, and failure.
- Develop high-fidelity and fit-for-purpose CFD and FSI models, including appropriate geometry, meshing, turbulence treatment, boundary conditions, coupling strategies, material behavior, and solution controls.
- Model wind and tornado loads on large buildings and other structures, including wind effects on rooftop equipment, ground-mounted and rooftop photovoltaic (PV) systems, wind turbines, cooling towers, stacks, and other exposed components.
- Evaluate wind, flood, wave, and current loads on structures and infrastructure, as well as flow-induced vibration and aerodynamic instability.
- Couple CFD with finite element and structural dynamics models to predict stresses, deformation, fatigue, buckling, damage progression, and failure under operational and extreme events.
- Use available wind-tunnel, laboratory, component, or system-level experiments to calibrate and validate CFD, FSI, and reduced-order models.
- Assess model sensitivity, uncertainty, verification, validation, strengths, and limitations, and communicate their implications for engineering and risk decisions.
- Integrate simulation results with structural reliability, vulnerability and fragility modeling, natural hazard risk assessment, and loss estimation.
- Develop and evaluate mitigation measures, improve structural or equipment resilience, and support property loss prevention.
- Collaborate with scientists, engineers, modelers, business stakeholders, academic partners, and industry organizations to define research needs and implement results.
- Prepare high-quality technical reports, presentations, scientific publications, engineering guidance, and recommendations for technical and nontechnical audiences.
- Support research project planning, execution, documentation, quality assurance, and implementation.
Qualifications
Ph.D. in Mechanical Engineering, Structural Engineering, Civil Engineering, or a closely related field required; plus 3 years of relevant experience preferred,
or Master’s degree in one of these fields plus at least 5 years of relevant experience considered, 10 years strongly preferred.
Experience
- A strong background in CFD and demonstrated experience developing and validating coupled fluid–structure interaction (FSI) and multi physics models are required.
- Experience with CFD, structural, and multi physics software such as ANSYS Fluent/CFX, OpenFOAM, STAR-CCM+, Abaqus, or comparable platforms.
- Strong scientific programming and data-analysis skills using Python, C++, MATLAB, or similar tools.
- Experience verifying and validating simulations using analytical solutions, benchmark cases, wind-tunnel or laboratory experiments, field measurements, or operational data; identifying model assumptions, strengths, limitations, and uncertainty; and clearly explaining their effects on engineering, risk, and loss estimates.
- Experience applying finite element analysis and structural dynamics to evaluate stress, deformation, vibration, fatigue, buckling, damage, or failure.
- Experience in one or more application areas, such as wind engineering and the built environment or renewable energy generation and storage.
- Familiarity with artificial intelligence (AI), machine learning, reduced-order modeling, uncertainty quantification, sensitivity analysis, or optimization for simulation-intensive engineering problems.
- Experience with structural reliability, vulnerability or fragility development, probabilistic analysis, catastrophe risk modeling, or loss estimation is an asset.
- Knowledge of the analysis and design of reinforced concrete, steel, and wood buildings and foundations for wind, tornado, earthquake, snow, and flood loads is preferred; candidates willing to develop this knowledge through training will also be considered.
- Excellent written and verbal communication skills, including the ability to explain complex simulation methods and results to different audiences.
- Ability to take initiative, work independently, and collaborate effectively within an interdisciplinary research environment.
The hiring range for this position is $121,000 - $198,000 a year. The final salary offer will vary based on geographic location, individual education, skills, and experience. The position is eligible to participate in FM’s comprehensive Total Rewards program that includes an incentive plan, medical, dental and vision insurance, life and disability insurance, well-being programs, a 401(k) and pension plan, career development opportunities, tuition reimbursement, flexible work, and time off, including vacation and sick time.
FM is an Equal Opportunity Employer and is committed to attracting, developing, and retaining a diverse workforce.
Salary Context
This $121K-$198K range is in the lower quartile for Research Scientist roles in our dataset (median: $199K across 63 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 9,079 AI roles we're tracking, Research Scientist positions make up 2% of the market. At FM, 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 749 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($159K) sits 28% below the category median. Disclosed range: $121K 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.
FM AI Hiring
FM has 1 open AI role right now. They're hiring across Research Scientist. Based in Norwood, MA, US. Compensation range: $198K - $198K.
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 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
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