Founding Machine Learning Scientist at Tabula Bio in San Francisco, CA

About Tabula

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 matters on its own. Antibiotic resistance is a large and growing global problem, and new approaches are badly needed. But we also think this is the beginning of something broader.

Most biotech companies are mostly biologists with a small computational team attached. We think that model is going to look dated. Our view is that drug and therapy discovery will become much more computational over time, and we are building Tabula around that belief from day one.

Two things make this a particularly interesting problem for machine learning. First, we own our data generation loop. We are not just consuming static datasets and hoping they are good enough. We can generate new data, learn from it, and improve the system over time. Second, phages are one of the rare places in biology where the path from model output to human impact can be unusually short. We picked this area in part because, relative to most of biotech, the feedback loop to real-world use is fast.

One way to describe what we are doing is simple: we are using machine learning to help design living therapies that can save lives. That sounds a little like science fiction, which is part of why we think it is worth doing. But it is also a very practical engineering and research problem.

The role

We are hiring a Founding ML Scientist to help build the machine learning core of the company.

This is a research-oriented ML role. We are looking for someone who is strong in modern machine learning, likes difficult technical problems, and wants to work on something where the connection between model quality and real-world impact is unusually direct.

You will work on model development, experimental design, evaluation, and iteration in close partnership with our wet lab. That collaboration is central to how we work. The lab exists in large part to generate and validate the data that improves our models. Over time, we expect that loop between model design, data generation, and biological validation to become one of the company’s core advantages.

We also picked this problem deliberately. A lot of biologically important ML work sits very far from actual deployment in humans. In many cases, even very strong model progress may take years to affect a patient. Phages are different. They give us a much shorter path from model output to something that can matter in the clinic. In bio time, that is unusually fast.

Because this is a founding role, the job is not just to run experiments inside an existing system. The job is also to help define what the system should be. You will help shape how we think about research direction, model quality, experimentation standards, and the relationship between the computational and biological sides of the company.

What you’ll do

• Design and run model experiments against difficult biological data
• Help define the research roadmap for how Tabula’s models should improve over time
• Work closely with the lab to shape what data gets generated and why
• Build better practices around experimentation, evaluation, and reproducibility
• Contribute to technical decisions around model design, training, and research direction
• Help a small team build an AI company whose output is not content or software alone, but real therapies for real patients

What we’re looking for

• Strong background in modern machine learning and deep learning
• Fluency in Python and PyTorch, plus comfort with the broader modern ML stack
• Ability to reason clearly about model architectures, training, evaluation, and tradeoffs
• Good research judgment about which ideas are worth testing and how to evaluate them
• Comfort working in an early-stage environment where the problems are hard and the path is still being defined
• Ability to work closely with domain experts outside ML
• Strong interest in applying machine learning to a problem in the physical world, not just at the software layer

We do not require prior biology experience. In fact, we do not think that is the main thing that matters for this role. We already have biology expertise in the company. What we need here is someone who brings strong ML judgment, learns quickly, and is excited to work closely with scientists on a hard interdisciplinary problem.

You might be a fit if

• You have done substantial model development work, not just built products on top of model APIs
• You like designing experiments and learning from ambiguous results
• You are excited by the idea of using AI for something more concrete than the usual software treadmill
• You want your work to be tied to outcomes in the real world
• You are interested in helping build a company where machine learning is central, not decorative

Why Tabula

There are easier problems to work on.

This one is technically hard, scientifically ambitious, and directly connected to the real world. It sits in an unusual spot: the work looks like serious machine learning work, but the output is not an ad system, a chatbot wrapper, or a productivity feature. The output is a therapy.

That means the feedback loop matters. The data matters. The experiments matter. And if we get it right, the result is not just a better model. It is a better way of building medicines.

And we chose phages partly because they compress time. In many areas of biology, even important technical progress may take many years to reach a human being. Here, the path from model building to real human impact is much shorter. If we do this well, the work does not disappear into a long chain of abstractions. It can affect patients on a timeline that is unusually fast for biology.

We are still early. That is part of the appeal. The role comes with a lot of room to shape how the work is done and where it goes. If you want to help build an AI-first therapeutics company from the beginning, we’d love to talk.