Software Engineer, Oncology

Position Summary:

Design, build, integrate, and sustain oncology software applications that improve access to information, reduce manual work, and help teams operate more efficiently across research and business operations.

• Design, build, deploy, and sustain full-stack applications, data services, workflow tools, and automation.
• Partner with scientists/engineers/informatics/business stakeholders to identify needs, clarify requirements, and deliver solutions.
• Improve efficiency by simplifying processes, reducing handoffs/duplicative data entry, and connecting fragmented systems.
• Build and maintain APIs, data models, and access layers across scientific/operational/enterprise/vendor sources.
• Integrate with lab systems, data pipelines, analysis environments, and enterprise platforms.
• Apply standards for data quality, provenance, versioning, security, and access control.
• Develop AI-enabled capabilities (e.g., retrieval, natural-language access, extraction/annotation/summarization).
• Use AI-assisted development while meeting code review/testing/security/ownership standards.
• Evaluate and recommend build vs. configure/integrate/reuse/purchase/process change/retirement.
• Use shared practices: version control, peer review, automated testing, CI/CD, containerization, observability, dependency management.
• Deploy/operate on cloud in partnership with Tech@Lilly and Information Security; ensure reliability, support, documentation, maintenance, and lifecycle planning.
• Mentor junior engineers/co-ops/scientist-developers as appropriate.

• BS/MS in CS/software engineering (or related) + 5+ years professional software engineering (equivalent education/experience accepted).

• Python (preferred) or other modern server-side language.
• Web apps/APIs with modern frameworks (Django/Flask/Rails).
• Front-end in JS/TS with component framework.
• Relational DB design/SQL, APIs, integration.
• Cloud (prefer AWS), containers, CI/CD.
• Git, code review, automated testing, production support.
• AI-assisted tools and familiarity with LLM APIs.
• Communicate with non-engineers; work independently and manage concurrent projects.
• Experience with data-intensive/scientific/operational apps; regulated environments; biological data/lab systems/pipelines.
• Workflow automation, enterprise integration, reporting/visualization; AI-enabled/agentic apps; data governance and controlled-access systems.

• company bonus (based on performance)
• 401(k)
• pension
• vacation
• medical/dental/vision/prescription
• flexible benefits
• life/death benefits
• time off/leave
• well-being (EAP, fitness, clubs)