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Strong employer brand, remote role, and metro location increase applicant density despite specialization.
Highly domain-specific CFD and physics-ML expertise limits cross-industry transferability.
Requires PhD/MTech and deep CFD and scientific-ML expertise, creating stringent filters.
Develop and apply AI/ML techniques specifically for CFD and multi-physics simulation challenges involving fluid dynamics, heat transfer, and turbulence.
Build and deploy physics-informed neural networks, surrogate and reduced-order models to accelerate simulation workflows while maintaining engineering accuracy.
Collaborate across research, product, and external teams to translate scientific ML innovations into scalable, production-ready tools for thermal and fluid system design optimization.
PhD or MTech in Mechanical Engineering, Aerospace Engineering, Applied Mathematics, Computer Science, Physics, or related with strong CFD or computational science focus.
Proven experience with physics-informed neural networks or equivalent scientific ML methods applied to engineering or scientific computing problems.
Strong programming skills in Python and working knowledge of scientific ML frameworks like PyTorch, TensorFlow, or JAX.
Work Experience Required: Not explicitly mentioned in the JD.
Expertise in computational fluid dynamics, numerical PDE methods, turbulence modeling, and heat transfer with a record of applying AI to these domains.
Experience with deploying complete ML workflows involving data preparation, training, validation, and model deployment preferably on HPC/GPU environments.
Ability to work cross-functionally with research and engineering teams and translate complex scientific concepts into usable engineering tools.