Komodo Health aims to reduce the global burden of disease by leveraging smarter use of data through the Healthcare Map, a comprehensive view of the U.S. healthcare system. The company seeks to build AI-native solutions that elevate both the products they bring to market and the way they are built, focusing on delivering healthcare analytics and insights.
Requirements
- A track record of building AI-powered systems that move rapidly from prototype to production.
- Strong proficiency with LLMs, prompt engineering, context engineering, agent orchestration, and multi-agent systems.
- Fluency in Python and hands-on experience with Gen AI frameworks (Chat Completions API, vLLM, Crew AI, Strands, etc.).
- True full stack engineering: You are fluent across modern front-end frameworks such as React or Vue and back-end systems like FastAPI, Django, or Flask.
- Deep understanding of AI/ML fundamentals, with applied experience solving real-world problems using an AI-first approach.
- Comfort building quick demos, debugging AI behavior, and translating ideas into functional agent workflows or model pipelines.
- The engineering discipline to test, refactor, and scale systems with reliability, observability, and maintainability in mind.
Responsibilities
- Designing and delivering AI-native products and tools that solve real healthcare challenges and optimize internal workflows.
- Building and extending AI features across the full stack to ensure AI capabilities are production-ready and integrated into Komodo’s platform.
- Driving the full product lifecycle by gathering requirements, shaping them into technical designs, and delivering complete features that connect AI systems to real customer and business value.
- Driving experimentation by developing working demos and iterating based on fast feedback from users and data.
- Prototyping rapidly using GenAI models, chaining techniques, and orchestration frameworks to test ideas in days—not quarters.
- Researching and applying state-of-the-art AI techniques—LLMs, agent-based systems, generative models, etc.—to both structured and unstructured datasets.
- Building internal systems that enhance developer productivity: think agents for code review, documentation generation, or dynamic prompt libraries.
Other
- Degree in Computer Science, Machine Learning, or a related field—or equivalent hands-on experience that speaks for itself.
- Strong communication and collaboration skills—able to work across technical teams and influence non-technical stakeholders.
- Open to NYC or San Francisco or US Remote locations.
- Experience with specific healthcare data modalities (e.g., claims, EHR, genomic data).
- Experience with distributed computing frameworks (e.g., Spark, Snowflake, Databricks) for large-scale data processing.
