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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
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Who can apply
Review the original listing for work authorization, qualifications and employer requirements.