Research Software Engineer - iBioFoundry, Carl R. Woese Institute for Genomic Biology

University of Illinois Urbana-Champaign•Urbana, IL

10d•$75,000 - $100,000•Onsite

About The Position

The NSF iBioFoundry operates a cloud-based biofoundry that automates basic and applied biological research using artificial intelligence and robotics. We are seeking an experienced, self-motivated Research Software Engineer to lead software development toward a fully autonomous laboratory capable of planning and executing experiments with minimal human intervention. You will help build the next generation of biofoundries: fully automated, self-driving, and remotely accessible to enable the broader scientific community to advance synthetic biology and related fields. We are looking for someone with hands-on experience in software architecture, AI-driven systems, and IT operations who thrives in a focused, high-ownership environment.

Requirements

Bachelor's degree in computer science, Artificial Intelligence, bioinformatics, computational biology, software engineering, or a related field.
2+ years of professional full-stack software development experience in relevant areas such as biology, chemistry, engineering, or similar.
Operation of Cloud infrastructure and managing containers, e.g., Docker, Kubernetes.
Include a substantive cover letter, updated resume/CV, portfolio examples demonstrating software design, IT operations, personal GitHub; and contact information for a minimum of three professional references.
Supply links or attachments clearly demonstrating each of these qualifications.
If your work is under a DNA, please indicate this rather than omitting the item.

Nice To Haves

MS or PhD in CS, AI, Engineering, or a related field.
Hands on experience with agent AI and/or physical AI systems.
Experience with laboratory hardware automation, e.g., Tecan, Beckman Coulter, Thermo Fisher.
Prior contributions to scientific open source software or AI for science projects.
Research-oriented mindset, comfortable with ambiguity and iterative experimentation.
Strong sense of ownership: identifies problems and drives solutions proactively.
Excellent communication skills and ability to work closely with biologists and engineers.
Enjoyment of mentoring students through code review and project guidance.
Solid software engineering fundamentals: version control, testing, modular design, and CI/CD.

Responsibilities

Implement, operate, and continuously improve a multi-agent AI system that plans biological research and translates high-level research objectives into executable experimental workflows.
Integrate AI-generated workflows with robotic systems and a central scheduler controlling laboratory instruments, including liquid handlers, thermocyclers, incubators, plate readers, and mass spectrometers.
Maintain and implement critical components of the software backbone supporting autonomous laboratory operations.
Write, test, document, and maintain failure-sensitive software with a strong emphasis on reliability, observability, fault tolerance, and safe operation.
Develop deep system-level expertise and take ownership of the technical integrity of core infrastructure.
Develop and maintain an open-source scheduler for experiment planning, instrument coordination, and laboratory automation in close collaboration with researchers and engineers.
Design scheduling infrastructure that can reliably coordinate complex, concurrent workflows across heterogeneous laboratory equipment.
Design software architecture and operational practices that enable rapid troubleshooting, clear failure diagnosis, and long-term system evolution.
Establish and promote engineering best practices for testing, monitoring, deployment, documentation, version control, and incident response in a complex research environment.
Build robust data pipelines, services, and APIs connecting AI agents, laboratory instruments, experiment-management systems, and scientific databases.
Ensure that experimental data and system state are reliably captured, accessible, traceable, and usable by both humans and AI systems.
As the program grows, you will have increasing responsibility for coordinating a team of graduate students and postdoctoral researchers.
Assign and prioritize technical work, review code and architectural decisions, establish software engineering best practices, and mentor team members through successful project delivery.