Staff AI/ML Systems Engineer for Hardware Design

Vinci; Palo Alto (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Vinci is building an AI assistant for hardware designers to accelerate design iterations. You will contribute to the architecture and pipelines that turn development into deployment, shaping data flow across the product.

You'll work on scalable features, ML infrastructure, and collaborate with early customers and design partners to define the roadmap and deliver essential capabilities for engineers.

Qualifications

• 6+ years of experience developing and shipping features in 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 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.

Responsibilities

• 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 products that engineers will love. Also learn about entrepreneurship!

Skills

HPC

C++

Python

gRPC

Protocol Buffers

Docker

Kubernetes

Bazel

Cloud computing

Frontend

Tools

Docker

Kubernetes

Bazel

gRPC

Job description

Vinci is building an AI assistant for hardware designers to accelerate design iterations. You will contribute to the architecture and pipelines that turn development into deployment, shaping data flow across the product.

You'll work on scalable features, ML infrastructure, and collaborate with early customers and design partners to define the roadmap and deliver essential capabilities for engineers.