Founding Machine Learning Engineer

Our mission is to cure diabetes and help everyone living with it stay healthy until we get there.

We are building GloGlo as both a consumer health company and an AI-native research company focused on diabetes. Our consumer products help people today, while generating the resources and distribution to fund our long-term research toward a cure.

Today, our consumer products include:

• GloGlo Gummies: delicious dextrose gummies designed to raise low blood sugar quickly. They are available through Amazon, TikTok Shop, and retailers.
• GloGlo AI food tracker: our iPhone and Android app that helps people track food, estimate carbs from a photo, and receive personalized AI coaching to support more time in their target glucose range.

And we are just getting started. We expect to launch many more consumer products across food, health, wellness, and software.

Our mission is to cure type 1 diabetes. GloGlo is building an AI-native therapeutics company in San Francisco, connecting computational research with experimental biology. Our research agents help scientists investigate the literature, analyze biological data, and develop hypotheses that can be tested in the lab.

We’re looking for a Founding Machine Learning Engineer to build the systems behind that work. You’ll work directly with the founders and scientists, turning research questions into reliable tools and using experimental evidence to improve them. This is a hands-on role spanning models, data, evaluation, and software engineering.

• Build AI agents that search scientific literature and work with research tools.
• Develop data pipelines that make scientific sources traceable and analyses reproducible.
• Design evaluations for model accuracy, evidence quality, and useful scientific reasoning. Investigate failures and use the results to guide improvements.
• Train, adapt, and evaluate models where they improve a concrete research task.
• Work with scientists to connect computational predictions with experimental results and identify the next useful questions.
• Take systems from prototype to dependable daily use, with attention to testing, observability, latency, and cost.

• Strong Python or TypeScript skills and experience building and shipping machine learning systems.
• Practical experience with modern ML frameworks, language models, retrieval, or agent tooling.
• A rigorous approach to evaluation, data quality, and debugging, including knowing when a result does not support a conclusion.
• Comfort owning open-ended problems and working closely with people from different scientific and engineering disciplines.
• Curiosity about biology and a commitment to our mission. Experience in scientific computing or biological data is especially useful.