Senior Scientist

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. The immediate problem is important on its own. Antibiotic resistance is a serious and growing challenge, and new approaches are needed. We think phages are one of the most interesting starting points.

But 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. That means 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.

That is what makes this role unusual. You are not joining a traditional biotech company where computation sits off to the side. You are joining a company where the experimental work directly shapes the learning process.

We are hiring a Scientist to help build our wet-lab capability in synthetic phage engineering.

This is a hands-on bench role. We are looking for someone who is strong in molecular biology, excited by synthetic biology, and interested in helping engineer and test bacteriophages in a fast-moving research environment. The work will include construct design and assembly, cloning, assay execution, experimental troubleshooting, and generating the data that powers our larger technical system.

You will work closely with the lab team in the San Francisco Bay Area and Madison, and in collaboration with the broader company, including computational counterparts in San Francisco. The role is highly experimental and highly collaborative. It is a good fit for someone who likes building, testing, iterating, and learning quickly from real experimental results.

• 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

• You have been doing real bench science recently and want to stay close to the work

• You like building things in the lab, not just analyzing them afterward

• You care about experimental quality and can troubleshoot effectively when things fail

• You are excited by the idea of working on engineered living therapies

• You want your work to feed directly into a larger system that combines wet-lab science and machine learning

There are easier lab jobs.

This one 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.

That means the lab work matters a lot. The quality of the constructs matters. The quality of the assays matters. The quality of the data matters. If we get those things right, the result is not just a better experiment. It is a better way to discover and build therapies.

We are still early. That means there is a lot of room to shape how the work gets done and where it goes. If you want to help build that from the beginning, we’d love to talk.