Job Description:
• Lead the creation of differentiable CFD frameworks in JAX, extending capabilities to complex geometries and non-periodic boundary conditions using immersed boundary methods
• Design, implement, and containerize code (Docker/enroot) to ensure reproducibility and scalability across multi-node GPU HPC environments
• Develop optimized parallel linear solvers (FFT or matrix decompositions) and gradient-based scripts to iteratively modify reactor designs based on catalyst activity
• Rigorously validate simulation accuracy against benchmark results and fixed-bed reactor configurations for both non-reactive and reactive flow regimes
• Partner with AI model developers to integrate property predictions and generate comprehensive technical reports summarizing algorithm scalability and differentiability
Requirements:
• PhD or MS in Computational Physics, Mechanical/Chemical Engineering, Computer Science, or equivalent
• 3+ years (including PhD) of hands-on experience in CFD code development, specifically on GPUs
• Proven ability to build scalable software on multi-GPU systems using JAX, PyTorch, CUDA, or MPI frameworks
• Full fluency in numerical linear algebra, PDE numerical methods, sparse/dense linear solvers, and immersed boundary methods
• Excellent Python programming skills, with JAX proficiency as a prerequisite
• Ability to manage multiple deliverables and produce clear technical documentation for milestone reviews
Benefits:
• Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions
• Retirement savings with company matching
• Paid parental leave
• Inclusive family-building benefits
• Flexible paid time off
• Company-wide seasonal breaks
• Support for flexible work arrangements that enable sustainable performance
• Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs