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Tabula Bio
San Francisco, California, United States • On-site
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Develop physics-based mathematical models and large-scale numerical simulations of phage-bacteria infection dynamics. Collaborate with the wet lab to validate these models against experimental data and help define the company's research roadmap.
Requires a Ph.D. in Physics or a related quantitative field with strong expertise in numerical methods and complex dynamical systems. Proficiency in Python and the scientific computing stack is essential, though prior biology experience is not required.
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.
The core of our scientific work is the development of physics-based mathematical models of phage–bacteria interaction dynamics — how phages locate, bind to, infect, and replicate within bacterial targets at the population level — together with the large-scale numerical simulation of those models to identify and refine candidate therapies. To support that modeling, we run an in-house wet lab that generates the experimental data used to calibrate, test, and validate the underlying physical models.
Two things make this a particularly interesting problem. 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 computational and physics-based modeling 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.
We are hiring a Research Scientist to help build the computational and physics-based modeling core of the company.
This is a research-oriented role for a computational physicist (or someone with an equivalent quantitative, simulation-heavy background). We are looking for someone who is strong in numerical methods, large-scale simulation, and the modeling of complex dynamical systems, who likes difficult technical problems, and who wants to work on something where the connection between model quality and real-world impact is unusually direct.
You will develop physics-based models of phage–bacteria infection dynamics, design and run large-scale numerical simulations, and validate those models against experimental data 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 scientifically important computational 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 an early 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.
Develop physics-based theoretical and computational models of phage–bacteria infection dynamics, applying numerical simulation, statistical mechanics, and optimization theory
Investigate the underlying biophysics of phage infection — binding kinetics, replication dynamics, and population-level interactions — and translate these physical phenomena into tractable mathematical and computational form
Design and implement numerical methods and software for large-scale simulation and analysis of these physical systems
Perform mathematical analysis of the underlying models, including numerical stability analysis, error analysis, and validation against experimental data
Analyze experimental data using mathematical and statistical methods to infer physical parameters and constrain the underlying models
Design and conduct computational experiments, in close coordination with our wet-lab scientists, to test and refine the models
Help define the research roadmap for how Tabula's models should improve over time, and build better practices around experimentation, evaluation, and reproducibility
Help a small team build an AI-first company whose output is not content or software alone, but real therapies for real patients
A Ph.D. in Physics or a closely related quantitative field (applied mathematics, computational science, or similar), or equivalent research experience
Strong background in numerical methods, large-scale simulation, and the modeling of complex dynamical systems, including systems governed by partial differential equations
Depth in one or more of: statistical mechanics, optimization theory, probabilistic and statistical methods, and numerical stability and error analysis
Experience with large-scale scientific computing and high-performance computing environments
Fluency in Python and the modern scientific-computing stack; familiarity with the modern machine learning stack (e.g., PyTorch) is a plus
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 your own field
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 quantitative and computational judgment, learns quickly, and is excited to work closely with scientists on a hard interdisciplinary problem.
You have done substantial modeling and simulation 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 applying physics and computation to 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 computational and physics-based research is central, not decorative
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 computational and physics research, 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.
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