We’re looking for a driven, hands-on ML/AI Engineer to help us push the boundaries of what's possible in AI-driven drug development. You’ll work on production-grade LLM-based systems, knowledge graphs, and machine learning pipelines—turning prototypes into powerful tools that make a real-world impact.
You'll collaborate across teams to design, build, and deploy intelligent systems that enhance how life sciences organizations make critical decisions. If you love working on technically challenging problems with direct impact in healthcare—this is the role for you.
This is a remote-first position, but we’d love it if you’re based in or near Boston.
What You'll Do:
- Design and Deploy LLM Systems: Develop scalable, production-ready LLM applications using frameworks like LangChain/LangGraph. Build robust RAG pipelines and integrate knowledge graphs for biological and clinical data.
- Full-Stack AI Engineering: Write maintainable, high-performance code and build clean APIs and services for machine learning applications.
- Data Engineering Collaboration: Work with data engineers to build and optimize data workflows and pipelines for high-quality data ingestion and processing.
- Product-Focused Prototyping: Collaborate with product and domain teams to rapidly prototype AI solutions, iterate based on feedback, and scale models for production.
- Model Deployment & MLOps: Use modern MLOps tools to deploy and monitor models in production environments (AWS preferred). Ensure scalability, observability, and resilience.
- Collaborative Innovation: Partner with engineering, data, and business teams to identify and develop high-value AI/ML applications.
- Continuous Learning: Stay ahead of the curve on emerging ML frameworks, GenAI capabilities, and healthcare technologies.