Scientific Software Developer, Data Foundry

Eli Lilly Eli Lilly · Pharma · San Francisco, CA +3

Scientific Software Developers are needed to build the data infrastructure, scientific tools, and lab automation integrations that power AI-native drug discovery. This role involves working with scientists to create prototypes, data pipelines, APIs, and workflow tools, with a focus on enabling autonomous AI agents and closed-loop experimentation through lab automation. The role is anchored in Architecture4Insight, with collaboration across Methods4Insight and Automation & Scale4Insight, and involves building scientific software consumed by other teams, including AI agents.

What you'd actually do

  1. Design, build, and maintain data processing pipelines for complex scientific datasets (chemical, biological, High throughput experiments, and automation-generated data), ensuring FAIR compliance and machine-actionability.
  2. Develop RESTful APIs and microservices providing unified programmatic access to LIMS, ELNs, instruments, data warehouses (Postgres, Redshift, Snowflake), and analytical databases.
  3. Build integrations connecting lab automation equipment, scheduling systems, and instrument data streams to Data Foundry’s infrastructure with proper metadata and execution traceability.
  4. Build and operate cloud-native components (AWS, Azure, or GCP) supporting containerized workflows (Kubernetes/Docker), infrastructure-as-code, CI/CD, and workflow orchestration (Prefect, Airflow, Nextflow).
  5. Build software enabling seamless interfacing between automation platforms and AI-driven experimental planning.

Skills

Required

  • Python
  • SQL
  • scientific software development
  • understanding of experimental data types and scientific workflows
  • RESTful APIs
  • data pipelines
  • microservices
  • cloud platforms (AWS, Azure, or GCP)
  • containerization (Docker/Kubernetes)
  • Git
  • DevSecOps standards
  • CI/CD
  • workflow orchestration

Nice to have

  • Java
  • C#
  • Go
  • TypeScript
  • pharmaceutical or biotech experience

What the JD emphasized

  • AI-native drug discovery
  • autonomous AI agents
  • agentic workflows
  • AI-driven experimental planning

Other signals

  • AI-native drug discovery
  • autonomous AI agents
  • lab automation and agentic workflows
  • scientific software that other teams—including the Frontier AI group’s autonomous agents—consume