{"schemaVersion":"jobsearcher.job.v1","id":"335a7a3723aa16cb30e14e01","url":"https://jobsearcher.com/jobs/335a7a3723aa16cb30e14e01","canonicalUrl":"https://jobsearcher.com/jobs/335a7a3723aa16cb30e14e01","title":"Computational Neuroscientist","description":"About TBC\nThe Biological Computing Co. (TBC) is an applied biological computing company that uses real neurons to improve AI models.\nWe study how biological neural networks process information, extract useful computational principles and translate those insights into software that makes modern AI models better, faster and more efficient. Our Algorithm Discovery Platform brings together biology, computational neuroscience, AI research and software engineering to develop new algorithms, architectures and neurally-optimized software for generative video and next-generation AI infrastructure.\nToday, we are commercializing neurally optimized models that run on conventional GPU and cloud infrastructure. Longer term, we are building toward real-time biological compute, where real neurons operate alongside silicon as part of the compute stack.\nOur interdisciplinary team includes researchers and engineers with experience at Apple, Johns Hopkins, Meta, MIT, Stanford and other leading institutions.\n\nAbout the Role\n\nTBC is seeking a Computational Neuroscientist to help derive novel algorithms and model improvements for AI from understanding the dynamics of real neurons.\nYou will work across computational neuroscience, biology and machine learning to design experiments, analyze large-scale neural recordings and build models that connect living neural systems with modern foundation models. Your work will sit at the center of TBC’s Algorithm Discovery Platform: identifying where AI models fail, studying how biological neural networks approach related problems and translating what we learn into usable software.\nThis is a hands-on, high-ownership role for someone who wants to help define a new field. You will work closely with wet-lab biologists, AI researchers and engineers to move from experiment to mathematical principle to model performance.\n\nDesign biological computing experiments\nDesign experiments that encode temporal, spatial and multimodal information into living neural cultures.\nDevelop stimulation and information-encoding paradigms for high-density multi-electrode array systems.\nDefine experimental controls, baselines and validation criteria that distinguish useful biological effects from noise or generic dynamical behavior.\nPartner with the biology team to improve culture readiness, experimental consistency and reproducibility.\n\nAnalyze neural population dynamics\nAnalyze large-scale electrophysiological recordings from high-density MEAs and related neural-interface platforms.\nModel neural population dynamics, latent spaces, neural manifolds, temporal structure, effective connectivity and state transitions.\nDevelop methods for decoding neural responses and identifying computationally useful spatial and temporal patterns.\nCharacterize how neural networks respond, adapt, learn and retain information across different stimulation conditions and time scales.\n\nTranslate biology into AI systems\nWork with AI researchers to convert neural dynamics into mathematical principles, architectures, adapters, optimizers and learning rules.\nTest whether biologically derived principles improve generative video, world models, inference efficiency, continual learning, memory or generalization.\nCompare biological approaches against strong non-biological controls and surrogate models.\nDetermine which properties of the biological response are necessary for model improvement and which can be simplified for scalable software implementation.\nEvaluate discoveries across model sizes, datasets, architectures and modalities.\n\nBuild closed-loop research infrastructure\nHelp build tools for neural stimulation, real-time readout, experiment orchestration, data analysis and rapid iteration.\nDevelop reusable analysis pipelines and computational tools that connect wet-lab experiments with AI-model evaluation.\nSupport closed-loop systems in which model results inform biological experiments and biological measurements inform the next model iteration.\nContribute to TBC’s longer-term work in latent-space interfacing, neural controllability, connectome-guided learning and real-time biological inference.\n\nShape research strategy\nOwn research workstreams from hypothesis and experimental design through analysis, validation and technical communication.\nHelp define research priorities, technical milestones and decision criteria for TBC’s neuroscience programs.\nIdentify scientific, statistical and experimental risks before they become blockers.\nCommunicate findings clearly to biology, AI, engineering, product and company leadership.\nContribute to internal documentation, research publications, technical presentations and external scientific communications as appropriate.\n\nWhat Success Looks Like\nNeural experiments produce consistent, high-quality and interpretable population-level data.\nBiological observations are converted into testable computational hypotheses.\nValidated neural principles become software that produces measurable improvements in real AI models.\nResults hold up against strong controls, ablations and non-biological alternatives.\nExperimental and computational pipelines allow the team to move more quickly from question to evidence.\nTBC develops a clearer understanding of how biological networks represent, transform, learn and retain information.\nYour work advances both near-term neurally optimized software and the longer-term path to real-time biological compute.\n\nRequired Qualifications\nPh.D. or equivalent research experience in computational or systems neuroscience, neural engineering, machine learning, applied mathematics, physics, statistics or a related field.\nStrong background in neural-data analysis, neural population dynamics, neural coding or dynamical systems.\nExperience working with electrophysiology, MEA recordings, calcium imaging, brain-computer interfaces or comparable neural datasets.\nStrong programming ability in Python and experience with scientific-computing and machine-learning tools.\nExperience with several of the following:\nDimensionality reduction\nLatent-variable models\nNeural manifolds\nDynamical-systems modeling\nEncoding and decoding models\nTime-series analysis\nEffective-connectivity analysis\nStatistical modeling and uncertainty analysis\nAbility to design rigorous experiments and distinguish correlation from causal or mechanistic evidence.\nAbility to communicate clearly and work effectively with wet-lab scientists, AI researchers and engineers.\nStrong scientific judgment, ownership and comfort operating in a fast-moving research environment where the playbook is still being written.\n\nPreferred Qualifications\nExperience with closed-loop neural interfaces, adaptive stimulation or real-time neural decoding.\nExperience with causal inference, connectomics, synaptic plasticity, STDP or effective-connectivity estimation.\nFamiliarity with foundation models, generative video, world models, reinforcement learning or model-representation analysis.\nExperience with reservoir computing, neuromorphic computing, biological computing or other nontraditional compute substrates.\nExperience connecting population-level neural dynamics to machine-learning architectures.\nFamiliarity with PyTorch, JAX or other modern deep-learning frameworks.\nExperience building reusable research infrastructure, analysis pipelines or internal scientific tools.\nPublications at leading neuroscience, neural-engineering or machine-learning venues.\nInterest in translating frontier research into products that improve real AI systems.\nCompensation Range: $175K - $225K","company":"Biological Computing","rawCompany":"biological computing","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-08-08T14:03:46.076Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"19-3039.02","title":"Neuropsychologists","slug":"neuropsychologists"},{"code":"19-3039.03","title":"Clinical Neuropsychologists","slug":"clinical-neuropsychologists"}],"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":"Computational Neuroscientist","description":"About TBC\nThe Biological Computing Co. (TBC) is an applied biological computing company that uses real neurons to improve AI models.\nWe study how biological neural networks process information, extract useful computational principles and translate those insights into software that makes modern AI models better, faster and more efficient. Our Algorithm Discovery Platform brings together biology, computational neuroscience, AI research and software engineering to develop new algorithms, architectures and neurally-optimized software for generative video and next-generation AI infrastructure.\nToday, we are commercializing neurally optimized models that run on conventional GPU and cloud infrastructure. Longer term, we are building toward real-time biological compute, where real neurons operate alongside silicon as part of the compute stack.\nOur interdisciplinary team includes researchers and engineers with experience at Apple, Johns Hopkins, Meta, MIT, Stanford and other leading institutions.\n\nAbout the Role\n\nTBC is seeking a Computational Neuroscientist to help derive novel algorithms and model improvements for AI from understanding the dynamics of real neurons.\nYou will work across computational neuroscience, biology and machine learning to design experiments, analyze large-scale neural recordings and build models that connect living neural systems with modern foundation models. Your work will sit at the center of TBC’s Algorithm Discovery Platform: identifying where AI models fail, studying how biological neural networks approach related problems and translating what we learn into usable software.\nThis is a hands-on, high-ownership role for someone who wants to help define a new field. You will work closely with wet-lab biologists, AI researchers and engineers to move from experiment to mathematical principle to model performance.\n\nDesign biological computing experiments\nDesign experiments that encode temporal, spatial and multimodal information into living neural cultures.\nDevelop stimulation and information-encoding paradigms for high-density multi-electrode array systems.\nDefine experimental controls, baselines and validation criteria that distinguish useful biological effects from noise or generic dynamical behavior.\nPartner with the biology team to improve culture readiness, experimental consistency and reproducibility.\n\nAnalyze neural population dynamics\nAnalyze large-scale electrophysiological recordings from high-density MEAs and related neural-interface platforms.\nModel neural population dynamics, latent spaces, neural manifolds, temporal structure, effective connectivity and state transitions.\nDevelop methods for decoding neural responses and identifying computationally useful spatial and temporal patterns.\nCharacterize how neural networks respond, adapt, learn and retain information across different stimulation conditions and time scales.\n\nTranslate biology into AI systems\nWork with AI researchers to convert neural dynamics into mathematical principles, architectures, adapters, optimizers and learning rules.\nTest whether biologically derived principles improve generative video, world models, inference efficiency, continual learning, memory or generalization.\nCompare biological approaches against strong non-biological controls and surrogate models.\nDetermine which properties of the biological response are necessary for model improvement and which can be simplified for scalable software implementation.\nEvaluate discoveries across model sizes, datasets, architectures and modalities.\n\nBuild closed-loop research infrastructure\nHelp build tools for neural stimulation, real-time readout, experiment orchestration, data analysis and rapid iteration.\nDevelop reusable analysis pipelines and computational tools that connect wet-lab experiments with AI-model evaluation.\nSupport closed-loop systems in which model results inform biological experiments and biological measurements inform the next model iteration.\nContribute to TBC’s longer-term work in latent-space interfacing, neural controllability, connectome-guided learning and real-time biological inference.\n\nShape research strategy\nOwn research workstreams from hypothesis and experimental design through analysis, validation and technical communication.\nHelp define research priorities, technical milestones and decision criteria for TBC’s neuroscience programs.\nIdentify scientific, statistical and experimental risks before they become blockers.\nCommunicate findings clearly to biology, AI, engineering, product and company leadership.\nContribute to internal documentation, research publications, technical presentations and external scientific communications as appropriate.\n\nWhat Success Looks Like\nNeural experiments produce consistent, high-quality and interpretable population-level data.\nBiological observations are converted into testable computational hypotheses.\nValidated neural principles become software that produces measurable improvements in real AI models.\nResults hold up against strong controls, ablations and non-biological alternatives.\nExperimental and computational pipelines allow the team to move more quickly from question to evidence.\nTBC develops a clearer understanding of how biological networks represent, transform, learn and retain information.\nYour work advances both near-term neurally optimized software and the longer-term path to real-time biological compute.\n\nRequired Qualifications\nPh.D. or equivalent research experience in computational or systems neuroscience, neural engineering, machine learning, applied mathematics, physics, statistics or a related field.\nStrong background in neural-data analysis, neural population dynamics, neural coding or dynamical systems.\nExperience working with electrophysiology, MEA recordings, calcium imaging, brain-computer interfaces or comparable neural datasets.\nStrong programming ability in Python and experience with scientific-computing and machine-learning tools.\nExperience with several of the following:\nDimensionality reduction\nLatent-variable models\nNeural manifolds\nDynamical-systems modeling\nEncoding and decoding models\nTime-series analysis\nEffective-connectivity analysis\nStatistical modeling and uncertainty analysis\nAbility to design rigorous experiments and distinguish correlation from causal or mechanistic evidence.\nAbility to communicate clearly and work effectively with wet-lab scientists, AI researchers and engineers.\nStrong scientific judgment, ownership and comfort operating in a fast-moving research environment where the playbook is still being written.\n\nPreferred Qualifications\nExperience with closed-loop neural interfaces, adaptive stimulation or real-time neural decoding.\nExperience with causal inference, connectomics, synaptic plasticity, STDP or effective-connectivity estimation.\nFamiliarity with foundation models, generative video, world models, reinforcement learning or model-representation analysis.\nExperience with reservoir computing, neuromorphic computing, biological computing or other nontraditional compute substrates.\nExperience connecting population-level neural dynamics to machine-learning architectures.\nFamiliarity with PyTorch, JAX or other modern deep-learning frameworks.\nExperience building reusable research infrastructure, analysis pipelines or internal scientific tools.\nPublications at leading neuroscience, neural-engineering or machine-learning venues.\nInterest in translating frontier research into products that improve real AI systems.\nCompensation Range: $175K - $225K","datePosted":"2026-08-08T14:03:46.076Z","dateModified":"2026-08-08T14:03:46.076Z","hiringOrganization":{"@type":"Organization","name":"Biological Computing","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"335a7a3723aa16cb30e14e01"},"url":"https://jobsearcher.com/jobs/335a7a3723aa16cb30e14e01"}}