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Embed with discovery teams to translate scientific workflows into production AI systems. Rapidly prototype and deploy LLM/RAG-based solutions for multi-omics problems, run evaluation loops, ensure data provenance and reliability, and distill learnings into reusable platform components to drive adoption across therapeutic areas.

Lead strategy, development, and execution of AI products in clinical settings, ensuring compliance and innovation while collaborating with cross-functional teams.

Lead development, deployment, and validation of advanced analytical methods (e.g., cheminformatics, computational biology, AI/ML) to inform discovery decisions, integrate tools with data infrastructure and autonomous agents, mentor junior scientists, publish research, and collaborate across Data Foundry pillars to accelerate molecule discovery.

The Data Architect will design and build data infrastructure to support AI-driven drug discovery, focusing on data models, architectures, and integration strategies.

Lead the early-stage drug discovery community, focusing on user growth, contributor partnerships, and building relationships with biotech stakeholders. Responsible for platform activation and community engagement to accelerate AI-powered drug discovery.

The Senior Principal Scientist will design and conduct experiments using various biomedical assays to support drug discovery, focusing on data analysis and high-quality reproducible results using laboratory automation.

The Technical Lead will build software systems for AI-native drug discovery, design data pipelines, develop APIs, and collaborate with scientists to create efficient workflows and solutions.

The Data Architect will design data infrastructure for AI-native drug discovery, focusing on data models, ontologies, and data platform architecture to support scientific workflows and analytics.

Develop and apply AI/ML models (e.g., GNNs, Transformers) to drive drug discovery, collaborate with experimental scientists to iterate models and experiments, lead foundation-model development integrating molecular and biological data, publish findings, and work with MLOps/Data Engineering to transition prototypes into scalable tools.

The role requires engineering integrative solutions between AI and lab systems to enhance molecule discovery through automation and data connectivity. It involves prototyping, collaboration with scientists, deploying intelligent systems, and ensuring reliable lab operations.