Senior Research Engineer, CFD Algorithm Development

SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. They are seeking a hands-on computational scientist or engineer to lead the CFD Algorithm Development Project, which focuses on building AI-driven, physics-based optimization tools for next-generation catalytic reactors.

Responsibilities:

• Develop Differentiable Solvers: Lead the creation of differentiable CFD frameworks in JAX, extending capabilities to complex geometries and non-periodic boundary conditions using immersed boundary methods
• Scalable GPU Engineering: Design, implement, and containerize code (Docker/enroot) to ensure reproducibility and scalability across multi-node GPU HPC environments
• Numerical Optimization: Develop optimized parallel linear solvers (FFT or matrix decompositions) and gradient-based scripts to iteratively modify reactor designs based on catalyst activity
• Validate & Benchmark: Rigorously validate simulation accuracy against benchmark results and fixed-bed reactor configurations for both non-reactive and reactive flow regimes
• Cross-Functional Collaboration: 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
• PhD + 3 years of industry experience in a related computational field
• Proven expertise in non-isothermal flow, transport equations, and chemical reactor multi-physics (e.g., mass transport in fixed-bed systems)
• Expertise in adjoint optimization or auto-diff for gradient-based optimization of complex CFD objectives
• Experience with the full stack of scientific development, including CI/CD, unit testing, and deployment via K8s, Slurm, or Google Cloud Batch
• Demonstrated ability to deliver complex project milestones and publish high-quality technical results