January AI:

Founded by veteran business executive, Noosheen Hashemi, and Director of Genomics and Personalized Medicine at Stanford University, Dr. Michael Snyder, January AI is the first “precision health” company that combines continuous glucose monitoring along with heart rate, sleep, activity and food tracking, to enable you to see the impact of your diet and exercise on your body in real-time.

We help you understand your personal metabolic health, i.e. how your body metabolizes food, and make simple changes to avoid serious health issues down the road. The need for this has never been greater: 1 in 3 adults in the US are now pre-diabetic, and 80% are not even aware of it. January’s app and continuous glucose monitor gives you immediate insights on your diet and blood sugar levels, and provides personalized recommendations on how to adjust your diet and activity to improve your overall metabolic wellbeing. We help you build better habits in just 30 days.

At January AI, we believe that every day is January 1st, that every day is a fresh start. We believe that health isn’t a place you reach, but the simple process of understanding your body and doing just a little better than yesterday.

January AI is backed by Marc Benioff (CEO and founder of Salesforce), Jerry Yang (founder of Yahoo), and Steve Chen (founder of YouTube), among others.

Recent coverage: STAT, TechCrunch, New York Times, Wall Street Journal, Financial Times, and VentureBeat.

Responsibilities:

Architect and Productionize Advanced AI Systems: Lead the end-to-end development of production-grade AI systems. This includes designing, tuning, and deploying models that translate complex, multi-modal health data (genomics, continuous glucose monitoring, wearable data, nutrition logs) into actionable, personalized insights.
Innovate in Retrieval and Reasoning: Engineer and implement sophisticated Retrieval-Augmented Generation (RAG) pipelines. You will move beyond basic semantic search to develop novel strategies that fuse structured and unstructured data, enabling deep reasoning over a user's complete health profile.
Fine-Tune and Customize Foundational Models: Fine-tune and customize state-of-the-art language and multi-modal models (e.g. Llama 3, GPT, Gemma) on our proprietary datasets to enhance model accuracy, relevance, and safety for mission-critical health applications.
Build and Evaluate AI Agents: Develop, evaluate, and iterate on novel AI-powered features. This includes building multi-step AI agents capable of complex reasoning and tool use to guide users toward better health outcomes within our digital ecosystem.
Champion Machine Learning Excellence: Establish and champion best practices for the entire ML lifecycle. You will own data preprocessing, model evaluation, performance monitoring, and MLOps to ensure our solutions are robust, scalable, and scientifically valid.
Drive Research into Application: Stay at the forefront of AI research by actively consuming academic papers, medical research, experimenting with new architectures, and translating cutting-edge techniques into tangible product innovation.

Qualifications:

A BS degree in Computer Science, Software Engineering, or a related field is required.
4+ years of professional experience building and deploying machine learning models into a production environment.
High proficiency in Python and its core machine learning libraries (e.g. PyTorch, TensorFlow, Hugging Face, scikit-learn).
1-2 years of hands-on experience architecting solutions with Large Language Models (LLMs), including direct experience with models from OpenAI, Google, Anthropic, or open-source alternatives.
Proven experience with vector databases (e.g. Pinecone) and a strong grasp of the principles behind semantic search, retrieval, and ranking.
A proven track record of working with LLMs and building and optimizing RAG systems from first principles.
Solid software engineering fundamentals, including proficiency with SQL databases and experience working in a cloud environment (AWS, GCP).
A strong, quantitative background in the evaluation of ML models, including defining novel metrics, statistical validation, and designing A/B tests for a product setting.