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Salary: USD 145,000 - 170,000 / annual
Cellanome is a well-funded biotech startup tackling major challenges in biology through next-generation instrumentation and automation. The Staff Systems Integration Engineer is a core member of the Product Development team, reporting to the SVP of Product Development.
You will drive the integration of biological protocols with novel hardware, automating and troubleshooting existing protocols on a first-of-its-kind prototype instrument platform. This is a hands-on technical role that bridges biology, hardware, and software—requiring someone who can hit the ground running and collaborate closely with engineering and scientific teams.
Key responsibilities include:
- Designing experiments to validate prototype hardware performance
- Troubleshooting issues at the hardware-software-consumables-workflow interface
- Transferring existing molecular and cell biology protocols to the novel instrumentation platform
- Working cross-functionally with engineering and science teams to resolve protocol and assay issues
- Gathering requirements for downstream engineering and service teams
- Writing and maintaining automation, data-analysis, and instrument-control code using AI coding assistants
- Building AI-assisted tooling (scripted LLM workflows, agents, internal copilots) to accelerate optimization, experiment design, and failure-mode triage
- Applying AI/ML-assisted analysis to instrument and assay data to detect drift, classify failure modes, and shorten troubleshooting cycles
- Physical duties: lifting up to 40 lbs (~10% of time); travel up to 20% (including overnight/weekend and international)
The role emphasizes modern development practices—you're encouraged to leverage AI coding assistants and automation tooling to accelerate work.
REQUIREMENTS:
- Bachelor's degree in Biology, Biochemistry, Biomedical Engineering, or related field
- 5+ years of hands-on experience in molecular biology, cell biology, or biomedical lab environments
- Demonstrated ability to troubleshoot complex biological and instrumental workflows
- Experience with automation, scripting, or programming (Python, C++, or similar)
- Experience integrating AI/ML models into production labs or manufacturing workflows
- Experience working with biological workflows
