Excelsior Sciences leverages blocc chemistry — automated, AI-friendly small molecule synthesis — to accelerate drug discovery and scale from molecule to manufacturing. Backed by Khosla.
About the role
We are seeking a strong MS Computer Science candidate to join our Research Informatics / R&D IT team. You will help build the digital foundation that enables both scientists and autonomous AI agents—combining cloud-native data platforms, vendor LIMS/ELN and analytical systems, robust scientific data pipelines, and agentic AI / LLM capabilities.
What they're looking for
- Master’s degree in Computer Science (or a closely related field) with relevant coursework in cloud computing and the fundamentals of AI and ML
- Demonstrated experience building data pipelines, feature engineering, or scientific data workflows (e.g., Spark/Databricks-style pipelines, data quality checks, performance tuning)
- Hands-on experience with cloud platforms (AWS), containers (Docker/Kubernetes), and modern data/backend tools (SQL, PostgreSQL, orchestration frameworks)
- Strong proficiency with AI coding assistants and coding agents (e.g., Cursor, Claude Code, GitHub Copilot, or similar tools)
- Familiarity with LLM concepts, RAG, retrieval, or multi-agent systems (coursework, projects, or professional exposure)
- Willingness and aptitude to rapidly learn commercial LIMS/ELN or analytical platforms (e.g., Genedata, CDD Vault, Virscidian Analytical Studio), prior exposure is a plus
More about this role
We are seeking a strong MS Computer Science candidate to join our Research Informatics / R&D IT team. You will help build the digital foundation that enables both scientists and autonomous AI agents—combining cloud-native data platforms, vendor LIMS/ELN and analytical systems, robust scientific data pipelines, and agentic AI / LLM capabilities.
This role is ideal for someone with hands-on experience in cloud, data engineering, RAG/multi-agent systems, high-performance ML, and scientific computing who wants to apply those skills at the intersection of lab informatics and AI-native drug discovery. We leverage AI-agile software development and engineering practices as we aim to lead the field in advancing molecule discovery efficiently.
What success looks like: Scientific data becomes reliably FAIR and machine-actionable, enabling both human researchers and AI agents to drive faster closed-loop experimentation and accelerate molecule discovery.
- Integrate, extend, and support vendor Laboratory Information Management Systems (LIMS), Electronic Lab Notebooks (ELN), and analytical informatics platforms. Scope includes platforms such as Genedata, CDD Vault, Virscidian Analytical Studio,...
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