Machine Learning Infrastructure Engineer

About Us

We're building a co-pilot for hardware designers. Our mission is to enable 9M mechanical engineers to iterate through designs 1000x faster.

We are building our geometry + physics driven foundation model for each class of part design

We’ve raised a first round of capital from Khosla Ventures.

Responsibilities

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:

Architect and develop data generation pipeline and the services. We will generate geometry and physics solution data

Create a development and deployment pipeline for a product that includes on-prem deployment including: build system, containerization, data generation, indexing on GCP

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!

Qualifications

5+ years of experience developing and shipping features in a production environment

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

Startup experience is a strong advantage.

You’re a great fit for this role if you

Are excited about entrepreneurship, taking things from 0 to 1,

Have a continuous learning mindset.

Are interested in building a geometry and physics based model for a vast variety of design problems (e.g. heat dissipation system for chips to landing gears)!

Thrive when you have autonomy and ownership over your work

Location

We’re located in Palo Alto,, CA, planning to work 2-3 days in person. Benefits and perks

Benefits/Perks

Generous compensation & equity (stock options)

Health + dental + 401k

Lunch, drinks, and snacks provided for in-person days