Run full-resolution simulations in minutes. Vinci’s foundation model for physics unites AI acceleration with verified solvers for as-built accuracy. Backed by Khosla.
About the role
You’ll be a force multiplier for our team and will design and develop the pipelines and tools that make our product a product. This includes accelerating our development by developing the system that enables our code to go from development to deployment. You’ll also orchestrate how data flows throughout the deployed product. Specific tasks include: Develop key features to improve our product including: making it more scalable, developing improving APIs to enable more complex engineering workflows
What they're looking for
- 6+ years of experience developing and shipping features in the space of high performance computing
- Proficiency in C++, Python, or any other language necessary for setting up a system. Tools like grpc, protocol buffers, Docker, Kubernetes, Bazel (or your favorite language agnostic build system)
- Proficiency in using cloud compute be it for data generation, scraping, and enabling data science teams to train models with MLOps is a plus
- CUDA experience would be amazing but not required
- Frontend experience is a plus
- Startup experience is a strong advantage
More about this role
We're building an AI assistant for hardware designers. Our mission is to enable millions of hardware designers and engineers to iterate through designs 1000x faster.
We are building our geometry + physics driven foundation model for each class of part design.
We are looking for people who love to build new products to help us improve our MVP.
You’ll be a force multiplier for our team and will design and develop the pipelines and tools that make our product a product. This includes accelerating our development by developing the system that enables our code to go from development to deployment. You’ll also orchestrate how data flows throughout the deployed product. Specific tasks include:
Develop key features to improve our product including: making it more scalable, developing improving APIs to enable more complex engineering workflows
Set up and integrate LLM or VLM infrastructure
Set up an MLOps framework for training deep learning architectures for geometry and physics data
Assist in the strategy, planning the product roadmap, and prioritize the development in partnership with early customers and design partners
Build and ship critical product features
Learn a lot while building...
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