Advisor - Lab Automation Software Engineer, AI-Integration

Eli Lilly and Company

San Diego (CA)

On-site

USD 90,000 - 120,000

Full time

14 days+

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Job summary

A global biopharmaceutical leader in San Diego seeks a Lab Automation Integration expert to lead innovative software integrations enhancing lab workflows. The successful candidate will possess a Ph.D. or equivalent in a relevant field, extensive experience in Python, and proven skills in lab automation technologies. Key responsibilities include mentoring team members, deploying robust systems using Docker and Kubernetes, and integrating AI solutions. A collaborative spirit and curiosity in biologics discovery will drive success in this role.

Qualifications

  • Ph.D. in relevant discipline with a minimum of 3 years of industry experience.
  • M.S. with a minimum of 5 years of industry experience.
  • B.S. with a minimum of 8 years of industry experience.

Responsibilities

  • Lead design and implementation of lab software integrations.
  • Deploy containerized services using Docker and Kubernetes.
  • Mentor within the automation engineering team.

Skills

Python development skills
Lab automation integration
Experience with Docker and Kubernetes
Collaboration skills

Education

Ph.D. in Computer Science, Software Engineering, Bioengineering or related discipline
M.S. in a related discipline
B.S. in a related discipline

Tools

Docker
Kubernetes
LIMS platforms

Job description

In This Role, You Will Have The Opportunity To

Lab Automation Integration
  • Lead the design and implementation of reliable software integrations between orchestration layers and physical lab systems including liquid handlers, plate readers, robotic workstations, and analytical instruments.
  • Own and evolve the translation layer between high‑level workflow logic and low‑level instrument control.
  • Partner with automation engineers and biologists to ensure integrations are robust, maintainable, and production‑ready.
Platform & Infrastructure
  • Deploy and maintain containerized services using Docker and Kubernetes with GitOps and CI/CD practices.
  • Integrate cloud‑based orchestration frameworks with laboratory control systems and LIMS infrastructure.
  • Build scalable, production‑grade Python applications and data pipelines that connect experimental readouts with downstream analysis tools.
AI & Intelligent Automation
  • Collaborate with the team to explore and prototype how AI‑driven decision‑making can be incorporated into existing automation workflows.
  • Contribute to the team’s evolving approach to autonomous orchestration, bringing software engineering rigor to early‑stage AI integration efforts.
  • Stay current with developments in LLM tooling and agent frameworks and help evaluate what is practically applicable to the lab environment.
Technical Leadership
  • Serve as a technical authority and mentor within the automation engineering team, helping to grow capability across software, integration, and emerging AI systems.
  • Influence the broader technical roadmap for autonomous discovery systems at the San Diego site.
  • Evaluate tools, frameworks, and vendor solutions for strategic fit with the team’s long‑term vision.
What Success Looks Like
  • Lab automation systems are reliably connected, well‑instrumented, and generating clean data that flows seamlessly into digital workflows.
  • The team’s software architecture evolves from early‑stage integrations toward scalable, production‑grade infrastructure.
  • AI capabilities are thoughtfully and pragmatically incorporated into automation workflows as the field matures.
  • Your technical leadership accelerates the capability growth of the broader automation engineering team.
Basic Qualifications
  • Ph.D. in Computer Science, Software Engineering, Bioengineering, or related discipline with a minimum of 3 years of relevant industry experience.
  • M.S. in a related discipline with a minimum of 5 years of relevant industry experience.
  • B.S. in a related discipline with a minimum of 8 years of relevant industry experience.
  • Demonstrated experience integrating software systems with laboratory automation platforms is required across all levels.
Preferred Skills & Attributes
Software Engineering & Infrastructure
  • Strong Python development skills with proven experience building and maintaining production‑level applications (FastAPI, Redis, Flask, pytest, etc.).
  • Deep hands‑on experience with Docker, Kubernetes, and CI/CD pipelines in production environments.
  • Experience with cloud‑based orchestration frameworks such as Argo on Kubernetes.
Lab Automation & Integration
  • Direct experience integrating software control systems with lab automation platforms (liquid handlers, analytical instruments, robotic workflows).
  • Hands‑on familiarity with common biologics discovery instrumentation — liquid handlers (Beckman, Hamilton, HighRes, etc.), micro‑dispersers, plate readers, or integrated robotic workstations is a strong plus.
  • Familiarity with scheduling and execution software used in integrated lab environments (Cellario, Green Button Go, Director, SAMI, VWorks, Genera, or similar).
  • Experience with LIMS platforms, ELN systems, or laboratory data pipelines.
AI & Emerging Technologies
  • Curiosity about and some hands‑on experience with LLMs, agent frameworks, or AI‑driven workflow automation.
  • Familiarity with orchestration tools such as LangChain, LangGraph, or similar is a plus — deep expertise is not expected.
  • Demonstrated willingness to learn and experiment with new technologies as they emerge.
  • General: proven ability to move fluidly between architectural thinking and hands‑on engineering execution.
  • Strong collaboration skills — comfortable working across software, automation engineering, and biological science teams.
  • Genuine curiosity about biologics discovery and a desire to understand the science well enough to build systems that truly serve it.

Lilly is a proud EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.

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