Research Engineer

Insight Global; Town of Niskayuna (NY); On-site; USD 68,000 - 94,000; Full time

Job summary

Insight Global is seeking a Research Engineer, Multi-Physics Modeling and Scientific Machine Learning, to join a leading energy technology research organization in an onsite Niskayuna, NY office. You will bridge computational physics, AI, and high‑performance computing to develop modeling solutions for next‑gen energy systems.

Responsibilities include developing advanced multi‑physics modeling methods, executing high‑fidelity simulations, building AI‑enabled surrogate models, and validating

Qualifications

• PhD in Mechanical Engineering, Aerospace Engineering, or related field.
• Experience applying AI/ML to physics-based problems.
• Strong Python proficiency and HPC experience.
• Background in CFD, flow physics and multi-physics simulations.
• Experience with ANSYS or COMSOL and physics-informed ML.

Responsibilities

• Develop advanced multi-physics modeling methodologies.
• Execute high-fidelity simulations and computational analyses.
• Design and implement scientific machine learning solutions.
• Build AI-enabled surrogate models for complex spatiotemporal systems.
• Apply ML to fluid mechanics and flow physics challenges.
• Deploy and test algorithms on HPC clusters.
• Evaluate model accuracy and performance improvements.
• Collaborate with cross-functional teams and present findings.

Skills

Python

PyTorch

HPC

CFD

Fluid mechanics

AI/ML

Communication

Education

PhD in Mechanical Engineering or Aerospace

Tools

ANSYS/COMSOL

JAX

Fortran/C++

Job description

Research Engineer, Multi-Physics Modeling and Scientific Machine Learning

Pay Rate:

$49/hr - $68/hr

Relocation:

Local candidates preferred; must be willing to work in an onsite office-based environment in Niskayuna, NY

Job Description:

Insight Global is seeking a Research Engineer, Multi-Physics Modeling and Scientific Machine Learning for a leading energy technology research organization. This candidate will work at the intersection of computational physics, artificial intelligence, and high-performance computing to develop innovative modeling solutions for next-generation energy systems. The role focuses on applying scientific machine learning techniques to complex fluid dynamics and multi-physics problems, creating data-driven surrogate models that accelerate traditional simulation workflows while maintaining high levels of accuracy. The ideal candidate will have deep expertise in fluid mechanics, CFD, AI/ML, and HPC environments, along with a passion for advancing cutting-edge technologies in power generation, renewable energy, electrification, and other large-scale industrial applications. This is a highly visible research position offering the opportunity to collaborate with multidisciplinary teams and contribute to transformative energy innovations.

Day-to-Day:

• Develop advanced multi-physics modeling methodologies
• Execute high-fidelity simulations and computational analyses
• Design and implement scientific machine learning solutions
• Build AI-enabled surrogate models for complex spatiotemporal systems
• Apply machine learning to fluid mechanics and flow physics challenges
• Deploy and test algorithms on HPC clusters
• Evaluate model accuracy and computational performance improvements
• Conduct validation and verification studiesCollaborate with cross‑functional engineering and research teams
• Present findings to technical and non-technical stakeholders
• Document technical approaches, results, and recommendations
• Support next‑generation energy technology development programs

Must‑Haves:

• PhD in Mechanical Engineering, Aerospace Engineering, or related engineering/scientific discipline
• Strong foundation in fluid mechanics and flow physics
• Research experience in advanced computational methods for multiscale physics applications
• Demonstrated experience applying AI/Machine Learning to physics‑based or engineering problems
• Experience with scientific machine learning methodologies
• Proficiency in Python
• Experience with PyTorch or similar ML frameworks
• Experience deploying algorithms on High‑Performance Computing (HPC) clusters
• Experience with CFD, aerodynamics, turbulence modeling, heat transfer, combustion, reacting flows, or related fluid dynamics disciplines
• Experience with engineering simulation tools such as ANSYS or COMSOL
• Ability to quantify computational acceleration and accuracy improvements from AI‑enabled models
• Strong communication and technical documentation skills
• Ability to work within multidisciplinary research teams

Plusses:

• Experience with JAX
• Experience with Fortran and/or C++
• High‑fidelity CFD and multi‑physics simulation
• Experience with industrial‑scale energy systems
• Gas turbine, wind turbine, renewable energy, nuclear, or electrification experience
• Experience with high‑temperature and high‑pressure flow systems
• Published research, conference papers, or postdoctoral experience
• Experience with reactive flows and combustion systems
• Familiarity with complex geometries such as turbine blade flows