Research Informatics Software Engineer

Level : MS Level / Early Career

Location : New York City — primarily onsite with periodic remote flexibility.

About the Role

We’re looking for an MS-level Computer Science candidate to join a Research Informatics / R&D IT team at the intersection of scientific software, data engineering, cloud infrastructure, and AI.

This is an opportunity to help build the digital foundation supporting modern drug discovery — from cloud-native data platforms and laboratory informatics systems to data pipelines, LLM/RAG applications, and emerging agentic AI capabilities. The ideal candidate is technically strong, hands-on, and excited to apply modern software engineering and AI technologies to real-world scientific problems.

What You’ll Do

Build, integrate, and support LIMS, ELN, and analytical informatics platforms, working across data models, APIs, workflows, and scientific data flows.
Design scalable data pipelines and APIs that make scientific data FAIR, high-quality, and machine-actionable for researchers and AI systems.
Develop and support cloud-native infrastructure, primarily in AWS, using technologies such as Docker, Kubernetes, CI/CD, and workflow orchestration.
Prototype and productionize GenAI and agentic AI applications, including LLM agents, RAG/GraphRAG, retrieval pipelines, and multi-agent workflows.
Partner with scientists and engineering teams to translate research needs into reliable software, data products, and AI capabilities.
Apply strong software engineering practices around testing, schema design, performance, observability, and production deployment.
Help continuously improve the digital laboratory environment and its readiness to support increasingly AI-driven workflows.

What We’re Looking For

Master’s degree in Computer Science or a closely related technical field.
Strong programming skills in Python and SQL.
Experience building data pipelines, scientific workflows, or backend/data applications.
Hands-on experience with AWS, Docker/Kubernetes, PostgreSQL, and modern data engineering tools.
Exposure to AI/ML and LLM technologies, including RAG, retrieval, or multi-agent systems through coursework, projects, research, or professional experience.
Experience using modern AI coding assistants / coding agents such as Cursor, Claude Code, GitHub Copilot, or similar tools.
Strong interest in learning and working with scientific applications, laboratory data, and commercial informatics platforms.
Ability to work collaboratively across software engineering, data, and scientific teams.
Exposure to commercial LIMS/ELN or analytical platforms such as Genedata, CDD Vault, Virscidian Analytical Studio, or similar.

Nice to Have

Hands-on experience with LLM/agentic AI systems, RAG, GraphRAG, knowledge graphs, or multi-agent architectures.
Experience with PyTorch, Airflow, Prefect, or related ML/data tooling.
Experience with high-performance ML or scientific computing.
Familiarity with Neo4j or other knowledge-graph technologies.
Experience with production-grade software engineering, including CI/CD, testing, API development, schema design, and performance optimization.
Interest in applying modern AI and data engineering to laboratory workflows and drug discovery.