Flagship Pioneering Benefits Overview

Design, build, fine-tune, deploy, and monitor production machine learning systems focused on LLMs, RAG, agentic orchestration, and multimodal data. Own the full ML lifecycle, establish MLOps practices, guide architecture and technical standards, and collaborate with data engineering, product, scientists, and domain experts. Translate scientific and product requirements into reliable, scalable ML solutions while communicating technical tradeoffs to diverse stakeholders.

Lead the experience vision and architecture for an AI-native platform serving scientists. Design and ship end-to-end scientific workflows, define interactions with AI agents, build a coded design system, and guide AI-assisted design and development practices. Partner with product managers, engineers, ML researchers, computational biologists, and scientific users while communicating strategy to leadership. The role combines design strategy, hands-on prototyping, production UI, research, implementation partnership, and QA ownership.

Collaborate with scientific teams to identify high-impact AI opportunities, define future-backward implementation plans, assess data needs, and build and evaluate machine learning solutions. The intern will use scalable GPU resources, develop benchmarks, iterate across model approaches and scientific domains, and produce comprehensive analyses supporting new company creation.