Job Opportunities API

Why Atrix Medical affairs teams at pharma companies are drowning in unstructured data—field notes from medical science liaisons, conference takeaways, real-world insights from post-market studies, scattered across dozens of sources. By the time these insights reach clinicians, they're stale. We're fixing that. Atrix helps medical affairs teams extract insights from messy, unstructured sources and turn them into action—so emerging findings get to clinicians faster and drugs are delivered more effectively. We're working with top-20 pharma companies and winning head-to-head against larger competitors because we actually listen to customers and build what they need. Who we are A small, senior team with roots at Google, a16z, Plaid, and Kickstarter. Seed stage, onsite in NYC. Everyone here has built and scaled products. The role You'll be a core backend engineer building the services and infrastructure that power our AI platform. You'll design and implement scalable APIs, optimize database performance, build distributed task processing systems, and ensure our platform can handle the demands of enterprise pharma customers. What you'll own - Our FastAPI-based backend services, including REST APIs serving complex AI workflows - Database architecture and query optimization in PostgreSQL - Distributed task processing with Celery and Redis - Infrastructure and deployment pipelines on AWS (EKS, S3, SQS, Lambda) - Integration with LLM frameworks (LangChain, LlamaIndex) and vector databases - Backend features that power document processing, data enrichment, and real-time communication You bring - 4+ years with Python - 2+ years building production REST APIs with FastAPI, Django, or Flask - PostgreSQL experience including schema design - Familiarity with async Python patterns - Docker containerization experience - Some exposure to cloud infrastructure (AWS preferred) Bonus points - Redis and Celery for distributed task processing - Deep query optimization and database performance tuning - Kubernetes experience - Vector databases (Qdrant, Pinecone) or embeddings experience - LLM framework experience (LangChain, LlamaIndex) - Healthcare or life sciences domain experience