Nome is dedicated to ensuring that every patient, regardless of how rare their disease, has access to a tailored treatment. With over 300 million people globally affected by rare diseases, 95% lack approved therapeutic options. Nome's Operating System for Personalized Therapeutics™ revolutionizes the development of personalized medicine by leveraging AI, advanced therapeutic platforms, and a network of experts to streamline the process. By bridging the gap between diagnosis and action, Nome accelerates the timeline to deliver actionable treatment plans in weeks instead of years. We collaborate with patients, healthcare providers, and pharmaceutical companies to make personalized medicine the norm.
We are seeking an Applied AI Engineer (Senior/Head) for a full-time hybrid position based in the San Francisco Bay Area, with flexibility for some remote work.
You will specifically be working to develop AI agents covering target identification, research protocol development, endpoint and trial design, and rapid IND writing in the context of personalized therapeutic (n=1/few) development for rare genetic diseases.
Best fits will have:
- Healthcare data experience. Strong background with genomic and clinical datasets. Comfortable with heterogeneous biomedical data.
- GenAI, agentic AI, and systems integration. Hands-on building with LLMs and AI agents. Specific experience with prompt engineering, post training, and reinforcement learning is a plus.
- Rare disease mission alignment. Every program we run is for a real patient, often a child. This work is deeply personal to our team, and we're looking for people who feel the same way.
Please do NOT apply if you are focused on developing deep foundation models - we are looking for candidates who want to build and ship many agents into production to automate core research operations tasks - this means we are not currently looking to predict novel biology via AI and candidates who are looking for these roles are not good fits.
5+ years of experience, SF or San Diego based is preferred but hybrid work is possible.
