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About the Role

Tabula is building an AI-first therapeutics company, starting with bacteriophages to design better therapies for hard-to-treat infections. This role is hands-on bench work in synthetic phage engineering, directly shaping the learning process within a company that integrates wet lab and computational efforts.

Responsibilities

• Run core molecular biology and synthetic biology workflows related to phage engineering
• Help construct, assemble, and test engineered phage variants
• Execute host-phage assays and related experimental workflows with strong attention to data quality
• Troubleshoot failures in constructs, assays, and protocols and improve them over time
• Generate clean, useful experimental data that informs model development and research direction
• Contribute to the growth of a small lab working on a technically ambitious problem with real-world consequences

Requirements

• Recent, hands-on bench science experience
• Enjoy building things in the lab
• Care about experimental quality and can troubleshoot effectively
• Excited by the idea of working on engineered living therapies
• Desire for work to feed directly into a larger system combining wet-lab science and machine learning

Skills

• Molecular biology
• Synthetic biology
• Phage engineering
• Construct design and assembly
• Cloning
• Assay execution
• Experimental troubleshooting
• Data generation

Location

• San Francisco Bay Area
• Madison

Work Type

• Hands-on bench role
• Experimental
• Collaborative

About the Company

• Tabula is building an AI-first therapeutics company.
• We are starting with bacteriophages, natural predators of bacteria, and building the models and experimental systems needed to design better therapies for hard-to-treat infections.
• Antibiotic resistance is a serious and growing challenge, and new approaches are needed.
• We think phages are one of the most interesting starting points.
• The broader idea is bigger than phage therapy alone.
• We believe drug discovery is going to become much more computational over time.
• Tabula is being built around that belief from the beginning.
• The wet lab is not separate from the computational work; it is a core part of the system.
• We build models, generate data, test hypotheses, and improve the loop.
• This role sits at an unusual intersection of synthetic biology, phage engineering, and machine learning.
• The work is practical and technical, but it also points at a bigger idea: using computation to help design living systems that can treat disease.
• We are still early, with room to shape how the work gets done and where it goes.