Sr. AI / ML Research Engineer
Description - External
At Trane Technologies and through our businesses including Trane and Thermo King, we create innovative climate solutions for buildings, homes, and transportation that challenge what’s possible for a sustainable world. We’re a team that dares to look at the world’s challenges and see impactful possibilities. We believe in a better future when we uplift others and enable our people to thrive at work and at home. We boldly go.
We are seeking a Sr. AI / ML Research Engineer to help build next-generation physics-based AI capabilities for fluid and thermal systems. This role is ideal for a candidate who combines deep expertise in computational fluid dynamics, scientific computing, and modern machine learning to accelerate simulation, design optimization, and digital engineering workflows.
The ideal candidate will bring hands-on experience applying physics-informed neural networks, neural operators, surrogate modeling, reduced-order modeling, and related scientific ML methods to real engineering problems. They will work across research and product teams to translate advanced methods into robust, production-oriented solutions that improve simulation speed, fidelity, and decision-making.
What’s in it for you
Be a part of our mission!
As a world leader in creating comfortable, sustainable, and efficient environments, it is our responsibility to put the planet first. For us at Trane Technologies, sustainability is not just how we do business—it is our business. If you want to apply advanced AI to high-impact engineering systems and help define the future of physics-based product development, we invite you to join us.
In this role, you will
- Develop and apply AI/ML methods for CFD and multi-physics simulation problems, especially in fluid flow, heat transfer, turbulence, and system-level thermal management.
- Build physics-informed neural networks and related scientific ML approaches for forward modeling, inverse problems, parameter estimation, data assimilation, and hybrid simulation workflows.
- Create surrogate and reduced-order models that accelerate high-fidelity simulation while preserving engineering accuracy.
- Apply Design of Experiments methods to simulation campaigns, data generation strategies, sensitivity analysis, and efficient exploration of high-dimensional design spaces.
- Work with structured and unstructured simulation data from commercial and open-source solvers such as ANSYS Fluent, STAR-CCM+, OpenFOAM, Moldflow, or similar platforms.
- Design training, validation, and benchmarking workflows for scientific ML models using both simulated and experimental datasets.
- Partner with domain experts, software engineers, and product teams to deploy research into usable tools and scalable engineering workflows.
- Partner with external vendors and strategic technology providers to evaluate, adapt, and transition advanced AI/ML solutions into scalable in-house capabilities and engineering workflows.
- Contribute to technical strategy in physics AI, scientific machine learning, model validation, and engineering optimization.
- Communicating results clearly to technical and non-technical stakeholders and support adoption across the organization.
What you will bring
Required qualifications
- PhD or MTech in Mechanical Engineering, Aerospace Engineering, Applied Mathematics, Computer Science, Physics, or a closely related field with strong emphasis on CFD or computational science.
- Strong foundation in computational fluid dynamics, numerical methods for PDEs, turbulence modeling, and heat transfer.
- Demonstrated experience applying physics-informed neural networks or related methods to engineering or scientific computing problems.
- Strong programming skills in Python and experience with scientific ML frameworks such as PyTorch, TensorFlow, or JAX.
- Experience building end-to-end ML workflows including data preparation, training, evaluation, hyperparameter tuning, and model deployment.
- Experience working in Linux and HPC environments, including parallel computing and GPU-based training (desirable).
- Strong understanding of model verification, validation, uncertainty, and physical consistency in engineering applications.
- Excellent communication and collaboration skills with the ability to work across research and engineering teams.
Preferred qualifications
- Experience with neural operators, operator learning, graph neural networks, geometric deep learning, differentiable simulation, or hybrid physics-ML modeling.
- Experience integrating AI models with CFD solvers, optimization frameworks, or digital twin platforms.
- Knowledge of Design of Experiments, design optimization, Bayesian optimization, inverse design, or control for thermal-fluid systems.
- Familiarity with geometry/mesh pipelines, simulation automation, workflow orchestration, and injection molding or Moldflow-style simulation workflows.
- Track record of publications, patents, or production deployments in scientific ML, CFD, or physics-based AI.
We offer competitive compensation and comprehensive benefits and programs. We are an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, pregnancy, age, marital status, disability, status as a protected veteran, or any legally protected status.