# Our mission spans every market with physical products: **eliminate waste and accelerate growth** ### **Why Omnifold is the place to do your best work** #### Growing Fast in a Massive Market Within six months of getting started, we have: - **assembled a team** with backgrounds at foundation model labs, and prior nine-figure exits. Omnifold is built by AI PhDs, professors, and technologists from MIT, Stanford, Google, Palantir, Snowflake, and NASA. - **delivered results to customers** - from high-growth brands to publicly traded corporations - **raised $28 million** from top VCs #### Unique Technical Challenges - **Data:** We acquire, build, and curate proprietary and uncorrelated data assets which carry signal about complex real-world systems - **Modeling:** True intelligence requires modeling the physical processes which generated data, not just fitting increasingly complex curves through optimization pressure - **Compute:** When needed, we believe in deploying the capital needed to benefit from scaling compute ## **What makes Omnifold unique** #### [_Ishaan Nerurkar_](https://www.linkedin.com/in/ishaannerurkar/) _– CEO_ I believe that many generational companies are being founded in the AI space. However, most startups are competing to build slight variants of the same solution (coding assistants, vertical agents, AI infrastructure). **For these companies, distribution and speed are far more likely to drive success – not your technical contributions.** In order to build a true outlier company, it’s necessary to innovate outside of the consensus. It’s a common assumption that general intelligence will eventually handle every problem, but certain problems require specialized data, algorithms, and modeling of physical systems. **In the same way that autonomous driving and protein folding required Waymo or AlphaFold, supply chains need purpose-built intelligence**. At Omnifold, we're designing AI systems to optimize how physical products are built, stored, and distributed throughout the world. We're not the first company in this space, but to our knowledge **we are the only ones treating it as a fundamental computer science problem rather than an application layer problem**. Because of our unique approach to data, modeling, and interfaces, our ambition isn't just to compete – it's to be the last company this market will need. ## Open Positions (8) ### Engineering [**Head of Product Engineering** \ Engineering • San Francisco HQ • Full time • On-site](/content/careers?ashby_jid=ba55abee-5047-432f-a9b5-58dad88d9768/index.html) [**Infrastructure Tech Lead** \ Engineering • San Francisco HQ • Full time • On-site](/content/careers?ashby_jid=7cdf98f0-4657-439f-a9c9-01b7430fc229/index.html) [**MTS - Applications** \ Engineering • San Francisco HQ • Full time • On-site](/content/careers?ashby_jid=0a377902-3078-4e30-a824-ca382461aff7/index.html) [**MTS - Infrastructure** \ Engineering • San Francisco HQ • Full time • On-site](/content/careers?ashby_jid=0a697113-761d-4865-adf3-2143b65b1f0f/index.html) ### Marketing [**Field Marketing Manager** \ Marketing • San Francisco HQ • Full time • On-site](/content/careers?ashby_jid=1017eef8-f8e6-4e06-a851-37ae93107d5d/index.html) [**Head of Demand Generation** \ Marketing • San Francisco HQ • Full time • On-site](/content/careers?ashby_jid=61336e46-5326-4c69-94b7-4721efad7c2d/index.html) ### Research [**MTS - ML Research Engineer** \ Research • San Francisco HQ • Full time • On-site](/content/careers?ashby_jid=6a893139-07ee-46cd-bf84-9045445ee272/index.html) [**MTS - ML Research Scientist** \ Research • San Francisco HQ • Full time • On-site](/content/careers?ashby_jid=8edfac45-dc5e-4fba-9a03-2b64328db533/index.html) * * * ## [**Claude vs a Vending Machine**](/content/blog/why-a-world-class-ai-agent-couldnt-manage-a-vending-machine/index.html) **:** Read our analysis of why the simplest supply chain problem defeats today’s general intelligence