Brainco

Machine Learning Engineer, Applied AI

San Francisco, California, United StatesFull timePosted 12 days ago
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ABOUT BRAIN CO.

Brain Co. is an applied AI startup co-founded by Jared Kushner and Elad Gil, and backed by leading Silicon Valley builders including Patrick Collison and Andrej Karpathy.
We are building AI applications for the world’s most important institutions, delivering impact on real-world problems across governments, healthcare systems, and critical industries.

Our progress so far:

- Automated construction permitting for a sovereign government → 80% faster, unlocking $375M+ in value

- Optimized supply chains for a leading global energy company → 30% lower cost, 99% reliability, preventing $100M+ in losses

- Streamlined hospital patient care across national health systems → 40% better outcomes, 80% less admin work

Company momentum:

- Raised a $55M Series A from leading investors

- Built a team of 70+ AI experts from Tesla, Google DeepMind, NVIDIA, and Databricks

At Brain Co., we focus on applying frontier AI to real institutional challenges, working alongside governments, healthcare systems, and critical industries to modernize how essential services operate.
We are looking for leaders who want to help bring new technology into institutions that impact millions of people.

ABOUT THE ROLE

As a Machine Learning Engineer at Brain Co., you will play a crucial role in deploying state-of-the-art models to automate various real world problems in sectors such as healthcare, government and energy. Part of the role will involve turning research breakthroughs into practical solutions for various nation states. This role is your opportunity to make a significant impact by making AI technology both accessible and influential.

IN THIS ROLE, YOU WILL:

- Innovate and Deploy: Design and deploy advanced LLM models to tackle real-world problems, particularly in automating complex, manual processes in a range of real-world verticals.

- Optimize and Scale: Build scalable data pipelines, optimize models for performance and accuracy, and prepare them for ...