Advisor - Lab Automation Software Engineer

Eli Lilly Eli Lilly · Pharma · San Diego, CA

This role focuses on designing and building AI-integrated, closed-loop autonomous discovery ecosystems for biotherapeutics research. It involves orchestrating automated laboratory workflows, implementing ML models for optimization, and creating digital lab infrastructure. The primary output is an agentic system that proposes hypotheses, executes experiments, analyzes results, and iteratively refines designs.

What you'd actually do

  1. Design and develop software applications that orchestrate complex automated laboratory workflows and closed-loop learning systems.
  2. Build data pipelines and visualization tools that provide real-time insights into lab operations and experimental outcomes.
  3. Implement machine learning models for visual inspection systems, predictive maintenance, and process optimization that enhance operational efficiency.
  4. Create digital lab infrastructure including IoT integrations, digital lab twin models, and telemetry monitoring systems.
  5. Develop and maintain integrations across multiple automation schedulers, LIMS, and data platforms.

Skills

Required

  • Python
  • SQL
  • JavaScript
  • Ph.D. in Computer Science, Software Engineering, Bioengineering/Engineering with at least three years of industry experience OR M.S. with at least 10 years OR B.S. with at least 13 years
  • DevOps practices
  • cloud computing platforms (AWS, Azure)
  • version control tools (GitHub, GitLab)

Nice to have

  • C#
  • VBA
  • R
  • C++
  • MATLAB
  • Bash
  • statistical analysis tools (JMP, SAS, R)
  • business intelligence platforms (Tableau, Power BI, Spotfire)
  • database management systems (DBMS)
  • data warehousing
  • lab digitalization technologies (IoT device integration, digital lab twins, automated usage and error reporting telemetry metrics)
  • Benchling
  • LabGuru
  • PostgreSQL
  • Genedata
  • LIMS/ELN systems
  • life sciences
  • pharmaceutical research
  • high-throughput screening environments

What the JD emphasized

  • AI-integrated, closed-loop autonomous discovery ecosystem
  • intelligent systems propose hypotheses, execute experiments through advanced robotics, analyze results instantly, and iteratively refine and design the next set of experiments
  • minimal human intervention
  • harness the latest breakthroughs in AI, machine learning, and laboratory digitalization
  • autonomous lab systems and closed-loop learning frameworks is strongly preferred

Other signals

  • AI-integrated, closed-loop autonomous discovery ecosystem
  • intelligent systems propose hypotheses, execute experiments through advanced robotics, analyze results instantly, and iteratively refine and design the next set of experiments
  • implement machine learning models for visual inspection systems, predictive maintenance, and process optimization