AI Developer

About Massive Bio

Every cancer patient deserves access to treatment options. Massive Bio is an AI-powered precision medicine platform transforming how cancer patients discover and access clinical trials by eliminating the barriers of geography, financial constraints, and information asymmetry that have historically limited enrollment.

Founded in 2015 and headquartered in US, Massive Bio is scaling its impact globally by powering operations across multiple countries and bringing innovative cancer treatment options to a rapidly growing and diverse population of patients. Through our proprietary AI platform, we connect individuals to clinical trials worldwide and partner with leading pharmaceutical companies, contract research organizations (CROs), and healthcare systems to accelerate drug development and expand equitable access to cutting-edge therapies.

About The Role

We're looking for an AI Developer to join our engineering team and build intelligent healthcare agents on our Agent Studio platform. You'll design, develop, and deploy AI-powered microservices that process medical records, match patients to clinical trials, and integrate with healthcare data standards, all within a modern, event-driven architecture.

This is a hands-on engineering role where you'll write production code daily, work with LLMs, vector databases, and healthcare data formats, and ship features that directly impact patient outcomes.

What You'll Do

• Build AI agents as Python microservices using our SDK (massivebio-agent-sdk)
• Design and implement multi-agent pipelines for medical data processing (OCR, clinical abstraction, terminology mapping, FHIR generation)
• Integrate LLMs (GPT-4o, GPT-5) for clinical text extraction, summarization, and decision support
• Work with vector databases (Qdrant) for semantic search, RAG, and terminology resolution
• Build and maintain integrations with healthcare systems (Azure FHIR, HL7, SNOMED CT, RxNorm, LOINC)
• Develop dashboard features for agent management, monitoring, and deployment
• Write clean, tested, production-ready code with proper error handling and observability
• Collaborate with clinical teams to translate medical workflows into agent pipelines
  
  

Requirements

Must Have:

• 3+ years of professional software development experience
• Strong Python skills (async/await, Pydantic, FastAPI or similar frameworks)
• Experience with LLMs prompt engineering, function calling, RAG pipelines, or fine-tuning
• REST API design and implementation
• SQL proficiency (PostgreSQL preferred)
• Git workflow (branching, PRs, code review)
• Comfortable with Docker and containerized deployments
• Strong problem-solving skills and ability to work independently
  
  

Nice To Have

• Experience with healthcare data (FHIR, HL7, ICD-10, SNOMED CT, medical records)
• Azure cloud services (Container Apps, Service Bus, Cosmos DB, Key Vault, Blob Storage)
• Vector databases (Qdrant, Pinecone, Weaviate) and embedding models
• OpenTelemetry or distributed tracing experience
• TypeScript/React (Next.js) for dashboard frontend contributions
• Experience building event-driven architectures (message queues, pub/sub)
• CI/CD pipelines (Azure DevOps, GitHub Actions)
• Knowledge of clinical trials, oncology workflows, or biotech domain
• Experience with CRM integrations (HubSpot, Salesforce)
  
  

Tech Stack

Language: Python 3.12, TypeScript

Backend: FastAPI, Pydantic v2, asyncio

Frontend: Next.js 15, React 19, Tailwind CSS

AI/ML: GPT-4o/5, Azure OpenAI, LLM Gateway, RAG

Databases: PostgreSQL, Cosmos DB (MongoDB API), Qdrant

Messaging: Azure Service Bus (queues, topics)

Healthcare: Azure FHIR (R4), SNOMED CT, RxNorm, LOINC

Infrastructure: Azure Container Apps, Docker, Azure DevOps CI/CD

Observability: OpenTelemetry, Azure Monitor

Package Management: uv, Azure Artifacts (private PyPI)

How You'll Work

• Agents as microservices — each agent is a standalone Python service that extends BaseAgent and implements a single process() method
• Built-in platform services — agents access LLM Gateway, Vector DB, PostgreSQL, HubSpot CRM, and FHIR Server via SDK clients (self.llm, self.vectordb, self.postgres, etc.)
• Event-driven pipelines — agents communicate via Azure Service Bus queues and topics
• One-click deployment — push code to GitHub, deploy from the Agent Studio dashboard
• Full observability — execution tracking, token usage monitoring, distributed tracing across agent pipelines