{"schemaVersion":"jobsearcher.job.v1","id":"6f863eb3bf70cad8ded53d34","url":"https://jobsearcher.com/jobs/6f863eb3bf70cad8ded53d34","canonicalUrl":"https://jobsearcher.com/jobs/6f863eb3bf70cad8ded53d34","title":"Machine Learning Engineer, Connectomics","description":"About Us\nEon is building the infrastructure for large-scale connectomics data collection, reconstruction, and brain simulation. Our mission is to enable the safe and scalable development of brain emulation technology, beginning with digital twins of model organisms.\nWe are developing an end-to-end platform that spans tissue preparation, high-throughput microscopy, large-scale image processing, neural reconstruction, connectome-based modeling, and embodied simulation. We are looking for exceptional engineers and scientists who can help turn biological brain data into usable computational systems.\nRole\nWe are seeking a machine learning, software, or data engineer with strong experience in large-scale neuroscience data pipelines. The ideal candidate has worked with connectomics, volumetric imaging, segmentation workflows, manual or semi-automated proofreading pipelines, and large-scale n-dimensional image data.\nThis role will help build and optimize Eon’s connectomics reconstruction pipeline: from raw microscopy data to segmented neurons, synapses, connectivity maps, visualizations, and brain simulations. You will work on segmentation, affinity prediction, watershed/post-processing, data management, scalable visualization, and machine-learning experiments. You may also contribute to embodied simulations of animal models using connectome-derived neural architectures.\nThis is a hands-on role for someone who is comfortable moving between ML experimentation, production data infrastructure, scientific computing, and computational neuroscience.\nResponsibilities\nBuild, optimize, and maintain large-scale connectomics data pipelines for volumetric microscopy data.\nDevelop and improve machine learning workflows for image segmentation, affinity prediction, watershed/post-processing, synapse detection, and neural reconstruction.\nWork with large-scale n-dimensional image data, including TB- to PB-scale datasets.\nRun controlled ML experiments to improve segmentation accuracy, throughput, and reliability.\nCreate polished, compelling visualizations of connectomic data, neural activity, and reconstructed circuits.\nSkills\nStrong ability to create polished and engaging visualizations.\nNeuroglancer, BigDataViewer, Fiji/ImageJ, CloudVolume, TensorStore, Zarr, N5, DVID, CAVE, or related tools.\nAffinity prediction, watershed segmentation, flood filling networks, U-Nets, transformers for vision, or other computer vision models for biological image data.\nDistributed data processing, cloud infrastructure, GPU inference, and high-throughput ML pipelines.\nGPU kernel development experience is a definite plus.\nLarge-scale n-dimensional array processing in Python, C++, Java, or similar environments.\nStrong software engineering skills, including clean code, version control, testing, documentation, and reproducible workflows.\nExperience with large data systems, ideally at TB scale or above.\nExperience with computer vision, biological image segmentation, or volumetric data analysis.\nStrong communication skills and ability to collaborate with neuroscientists, microscopists, ML engineers, and data infrastructure engineers.\nRepresentative Projects\nBuilding Eon’s large-scale connectomics segmentation and proofreading pipeline.\nCreating efficient workflows for affinity prediction, watershed segmentation, synapse detection, and neuron reconstruction.\nDeveloping Neuroglancer-style visualization infrastructure for large expanded-brain datasets.\nSalary\n$180k–260k base for SF-based ML Connectomics roles; $150k–200k base for remote. Competitive equity included.","company":"Eon Systems","rawCompany":"eon systems","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-08-04T19:26:58.321Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"19-1029.01","title":"Bioinformatics Scientists","slug":"bioinformatics-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":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541714","title":"Research and Development in Biotechnology (except Nanobiotechnology)","slug":"research-and-development-in-biotechnology-except-nanobiotechnology"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer, Connectomics","description":"About Us\nEon is building the infrastructure for large-scale connectomics data collection, reconstruction, and brain simulation. Our mission is to enable the safe and scalable development of brain emulation technology, beginning with digital twins of model organisms.\nWe are developing an end-to-end platform that spans tissue preparation, high-throughput microscopy, large-scale image processing, neural reconstruction, connectome-based modeling, and embodied simulation. We are looking for exceptional engineers and scientists who can help turn biological brain data into usable computational systems.\nRole\nWe are seeking a machine learning, software, or data engineer with strong experience in large-scale neuroscience data pipelines. The ideal candidate has worked with connectomics, volumetric imaging, segmentation workflows, manual or semi-automated proofreading pipelines, and large-scale n-dimensional image data.\nThis role will help build and optimize Eon’s connectomics reconstruction pipeline: from raw microscopy data to segmented neurons, synapses, connectivity maps, visualizations, and brain simulations. You will work on segmentation, affinity prediction, watershed/post-processing, data management, scalable visualization, and machine-learning experiments. You may also contribute to embodied simulations of animal models using connectome-derived neural architectures.\nThis is a hands-on role for someone who is comfortable moving between ML experimentation, production data infrastructure, scientific computing, and computational neuroscience.\nResponsibilities\nBuild, optimize, and maintain large-scale connectomics data pipelines for volumetric microscopy data.\nDevelop and improve machine learning workflows for image segmentation, affinity prediction, watershed/post-processing, synapse detection, and neural reconstruction.\nWork with large-scale n-dimensional image data, including TB- to PB-scale datasets.\nRun controlled ML experiments to improve segmentation accuracy, throughput, and reliability.\nCreate polished, compelling visualizations of connectomic data, neural activity, and reconstructed circuits.\nSkills\nStrong ability to create polished and engaging visualizations.\nNeuroglancer, BigDataViewer, Fiji/ImageJ, CloudVolume, TensorStore, Zarr, N5, DVID, CAVE, or related tools.\nAffinity prediction, watershed segmentation, flood filling networks, U-Nets, transformers for vision, or other computer vision models for biological image data.\nDistributed data processing, cloud infrastructure, GPU inference, and high-throughput ML pipelines.\nGPU kernel development experience is a definite plus.\nLarge-scale n-dimensional array processing in Python, C++, Java, or similar environments.\nStrong software engineering skills, including clean code, version control, testing, documentation, and reproducible workflows.\nExperience with large data systems, ideally at TB scale or above.\nExperience with computer vision, biological image segmentation, or volumetric data analysis.\nStrong communication skills and ability to collaborate with neuroscientists, microscopists, ML engineers, and data infrastructure engineers.\nRepresentative Projects\nBuilding Eon’s large-scale connectomics segmentation and proofreading pipeline.\nCreating efficient workflows for affinity prediction, watershed segmentation, synapse detection, and neuron reconstruction.\nDeveloping Neuroglancer-style visualization infrastructure for large expanded-brain datasets.\nSalary\n$180k–260k base for SF-based ML Connectomics roles; $150k–200k base for remote. Competitive equity included.","datePosted":"2026-08-04T19:26:58.321Z","dateModified":"2026-08-04T19:26:58.321Z","hiringOrganization":{"@type":"Organization","name":"Eon Systems","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"6f863eb3bf70cad8ded53d34"},"url":"https://jobsearcher.com/jobs/6f863eb3bf70cad8ded53d34"}}