The Opportunity: We're looking for a
Principal ML Engineer to lead the architecture and development of Vumedi's AI-powered discovery platform.
This isn't about adding an AI feature to an existing product.
You'll help define how hundreds of thousands of physicians discover knowledge through intelligent retrieval, semantic search, personalized recommendations, and LLM-powered experiences.
Working directly with our CTO, you'll have significant influence over both technical strategy and implementation: from evaluating emerging AI technologies to designing production systems that deliver highly relevant educational content at scale.
This is a rare opportunity to apply modern AI techniques to a mission that directly improves patient care around the world.
What You'll Build: You'll own the technical direction behind Vumedi's next-generation discovery platform, including:
- Designing intelligent recommendation systems that personalize educational content for physicians.
- Building LLM-powered discovery experiences that understand clinical intent and not just keywords.
- Developing semantic search, retrieval, ranking, and recommendation pipelines using embeddings, vector search, and modern retrieval architectures.
- Designing production-grade knowledge graph RAG systems that combine medical knowledge with generative AI.
- Evaluating and implementing emerging AI technologies, frameworks, and infrastructure as the ecosystem rapidly evolves.
- Building scalable inference and retrieval services capable of serving physicians around the world.
- Partnering closely with Product, Data Science, and Engineering to rapidly prototype, iterate, and launch new AI capabilities.
- Defining the long-term technical architecture for AI across the Vumedi platform.
While this is a Principal-level architecture role, we're looking for someone who enjoys building. You'll move comfortably between whiteboard discussions, architectural design, experimentation, and production code.
What We're Looking For:We're less interested in checking every technology box than finding someone who has built intelligent discovery systems at scale.
Ideal candidates have experience building one or more of the following:
- Recommendation systems
- Search relevance and ranking
- Semantic search
- Retrieval-Augmented Generation (RAG)
- Vector search and embeddings
- Knowledge retrieval systems
- Personalized content discovery
- AI-powered user experiences
You may have worked on products involving:
- Content recommendations
- Marketplace ranking
- Search infrastructure
- Feed ranking
- Personalized discovery
- Knowledge graphs
- Enterprise search
- AI assistants
Technical Experience:We're excited by candidates who have experience with many of the following:
- Production experience building LLM-powered applications
- Retrieval pipelines, embeddings, and vector databases
- Modern AI frameworks and orchestration tools
- Distributed systems operating at meaningful scale
- Cloud-native architectures
- Python and modern backend development
- Experimentation, evaluation, and iterative model improvement
You don't need experience with every technology we use today. We care far more about your ability to solve difficult discovery and AI problems than familiarity with a specific framework.
Why This Role Is Different:Many companies are adding AI features.
We're rethinking how physicians discover knowledge.
Medical education presents one of the most challenging retrieval problems in AI:
- Massive amounts of highly specialized content
- Complex user intent
- Clinical context
- Constantly evolving medical knowledge
- High expectations for relevance and trust
You'll help define how AI can responsibly connect physicians with the information they need to improve patient care.
Learn more about Vumedi 
