{"schemaVersion":"jobsearcher.job.v1","id":"a95df8a4e700c494409c86ed","url":"https://jobsearcher.com/jobs/a95df8a4e700c494409c86ed","canonicalUrl":"https://jobsearcher.com/jobs/a95df8a4e700c494409c86ed","title":"Research Scientist","description":"About Tabula\n\nTabula is building an AI-first therapeutics company.\n\nWe 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. Theimmediate 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.\n\nMost 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.\n\nThe 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.\n\nTwo 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.\n\nOne 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.\n\nThe role\n\nWe are hiring a Research Scientist to help build the computational and physics-based modeling core of the company.\n\nThis 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.\n\nYou 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.\n\nWe 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.\n\nBecause 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.\n\nWhat you'll do\nDevelop physics-based theoretical and computational models of phage-bacteria infection dynamics, applying numerical simulation, statistical mechanics, and optimization theory\nInvestigate 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\nDesign and implement numerical methods and software for large-scale simulation and analysis of these physical systems\nPerform mathematical analysis of the underlying models, including numerical stability analysis, error analysis, and validation against experimental data\nAnalyze experimental data using mathematical and statistical methods to infer physical parameters and constrain the underlying models\nDesign and conduct computational experiments, in close coordination with our wet-lab scientists, to test and refine the models\nHelp define the research roadmap for how Tabula's models should improve over time, and build better practices around experimentation, evaluation, and reproducibility\nHelp a small team build an AI-first company whose output is not content or software alone, but real therapies for real patients\nWhat we're looking for\nA Ph.D. in Physics or a closely related quantitative field (applied mathematics, computational science, or similar), or equivalent research experience\nStrong background in numerical methods, large-scale simulation, and the modeling of complex dynamical systems, including systems governed by partial differential equations\nDepth in one or more of: statistical mechanics, optimization theory, probabilistic and statistical methods, and numerical stability and error analysis\nExperience with large-scale scientific computing and high-performance computing environments\nFluency in Python and the modern scientific-computing stack; familiarity with the modern machine learning stack (e.g., PyTorch) is a plus\nGood research judgment about which ideas are worth testing and how to evaluate them\nComfort working in an early-stage environment where the problems are hard and the path is still being defined\nAbility to work closely with domain experts outside your own field\nWe 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.\n\nYou might be a fit if You have done substantial modeling and simulation work, not just built products on top of model APIs\nYou like designing experiments and learning from ambiguous results\nYou are excited by the idea of applying physics and computation to something more concrete than the usual software treadmill\nYou want your work to be tied to outcomes in the real world\nYou are interested in helping build a company where computational and physics-based research is central, not decorative\nWhy Tabula\n\nThere are easier problems to work on.\n\nThis 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.\n\nThat 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.\n\nAnd 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.\n\nWe 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.","company":"Tabula","rawCompany":"tabula","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-06-28T04:20:14.794Z","occupations":[{"code":"19-2012.00","title":"Physicists","slug":"physicists"},{"code":"19-2099.00","title":"Physical Scientists, All Other","slug":"physical-scientists-all-other"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"541714","title":"Research and Development in Biotechnology (except Nanobiotechnology)","slug":"research-and-development-in-biotechnology-except-nanobiotechnology"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Research Scientist","description":"About Tabula\n\nTabula is building an AI-first therapeutics company.\n\nWe 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. Theimmediate 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.\n\nMost 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.\n\nThe 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.\n\nTwo 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.\n\nOne 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.\n\nThe role\n\nWe are hiring a Research Scientist to help build the computational and physics-based modeling core of the company.\n\nThis 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.\n\nYou 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.\n\nWe 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.\n\nBecause 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.\n\nWhat you'll do\nDevelop physics-based theoretical and computational models of phage-bacteria infection dynamics, applying numerical simulation, statistical mechanics, and optimization theory\nInvestigate 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\nDesign and implement numerical methods and software for large-scale simulation and analysis of these physical systems\nPerform mathematical analysis of the underlying models, including numerical stability analysis, error analysis, and validation against experimental data\nAnalyze experimental data using mathematical and statistical methods to infer physical parameters and constrain the underlying models\nDesign and conduct computational experiments, in close coordination with our wet-lab scientists, to test and refine the models\nHelp define the research roadmap for how Tabula's models should improve over time, and build better practices around experimentation, evaluation, and reproducibility\nHelp a small team build an AI-first company whose output is not content or software alone, but real therapies for real patients\nWhat we're looking for\nA Ph.D. in Physics or a closely related quantitative field (applied mathematics, computational science, or similar), or equivalent research experience\nStrong background in numerical methods, large-scale simulation, and the modeling of complex dynamical systems, including systems governed by partial differential equations\nDepth in one or more of: statistical mechanics, optimization theory, probabilistic and statistical methods, and numerical stability and error analysis\nExperience with large-scale scientific computing and high-performance computing environments\nFluency in Python and the modern scientific-computing stack; familiarity with the modern machine learning stack (e.g., PyTorch) is a plus\nGood research judgment about which ideas are worth testing and how to evaluate them\nComfort working in an early-stage environment where the problems are hard and the path is still being defined\nAbility to work closely with domain experts outside your own field\nWe 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.\n\nYou might be a fit if You have done substantial modeling and simulation work, not just built products on top of model APIs\nYou like designing experiments and learning from ambiguous results\nYou are excited by the idea of applying physics and computation to something more concrete than the usual software treadmill\nYou want your work to be tied to outcomes in the real world\nYou are interested in helping build a company where computational and physics-based research is central, not decorative\nWhy Tabula\n\nThere are easier problems to work on.\n\nThis 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.\n\nThat 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.\n\nAnd 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.\n\nWe 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.","datePosted":"2026-06-28T04:20:14.794Z","dateModified":"2026-06-28T04:20:14.794Z","hiringOrganization":{"@type":"Organization","name":"Tabula","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"a95df8a4e700c494409c86ed"},"url":"https://jobsearcher.com/jobs/a95df8a4e700c494409c86ed"}}