About the Role

Founding ML Engineer role focused on building and scaling the world's first Physics Foundation Model. Responsibilities include developing scalable training approaches, designing new architectures, and optimizing distributed ML systems. The role requires strong production ML experience and expertise in large-scale, physics-informed AI.

Key Responsibilities

• Developing scalable approaches to train high-accuracy, large-scale physics-informed models
• Designing and testing new architectures for multi-physics and multi-scale modeling
• Optimizing training efficiency through distributed computing, GPU optimization, and performance tuning
• Building and productionizing large-scale, physics-informed ML systems
• Scaling training pipelines, APIs, or ML infrastructure that supports real-world products

Required Skills & Qualifications

Must Have:

• Strong Python + PyTorch (or JAX/TF) skills
• Proven experience delivering ML systems into production
• Experience with multi-GPU / multi-node training
• Hands-on AWS experience (compute, storage, IAM, cuda) or other cloud infra provider

Nice to Have:

• Exposure to engineering simulations (CFD, FEM, PDEs) or physics-informed ML (PINNs, neural operators)
• Familiarity with Docker/Kubernetes, CI/CD, Slurm, or other workload managers
• Experience with Graph Neural Networks, Physics-Informed Neural Networks, Neural Operators, and Transformer architectures