What You Will DoYour north star will be production and delivering value to our customers.
In this role you will leverage the Vinci framework to work on a variety of novel applications. Experiment with temporal propagation schemas, both in the classical and learned sense. You will use pre-existing data generation infrastructure to generate and curate training and verification samples. You will take modest iterative steps to prove out new utility and iterate hand in hand with customers to harden features.
You will work with Physicists, AI researchers, Software Engineers and Computational Geometry experts. You will work with a team to carry early prototypes through iteration and hardening all the way to customer use.
What We're Looking ForQualifications;
- Have published work leveraging or building a simulation practice.
- MS/MSc with 4+ years experience or
- PhD with 2+ years experience
- 2+ years using or building physics simulators
- FEM, FEA, Molecular Dynamics, FDTD
- Applied machine learning approaches
- Computer Vision, GraphNN, Transformer Architectures
- Working knowledge of modern ML basics
- back prop, loss functions, generators, embeddings, transformer models
- Experience with some modern ML practices
- PyTorch, Numpy, Cuda
- Training & Evaluation practices
- Have contributed to a production data processing system.
We are very excited to talk with you if you have
- Applied ML to the problem of
- Meshing geometry
- Accelerating FEM or DFT simulations
- Experience going from early stage prototype moving to a production environment at a Startup or National Lab
- Have leveraged simulation for design or data generation purposes.
Engineering Expectations- Software engineering fundamentals
- Comfortable meeting software design standards to get code into a production environment.
- Capable of leveraging pre-existing infrastructure and "closing the gap" on occasion
- Strong CI, regression testing, and validation discipline
- Comfort evolving core model infrastructure
