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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer, Connectomics - **Company:** EON, INC - **Location:** San Francisco, CA, United States - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Java (Programming Language), Data Analysis, Computer Vision, C++ (Programming Language), Cloud Computing, Image Analysis, Data Infrastructure, Distributed Computing Environment, Python (Programming Language), Linux Kernel, Machine Learning, Operational Databases, Scientific Computating, Software Engineering, Graphics Processing Unit (GPU), Data Management, Machine Learning Operations, Software Version Control, Data Pipelines - **Published:** July 11, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=9fbd7dc3822464c9 ## About the Role * Strong ability to create polished and engaging visualizations. * Neuroglancer, BigDataViewer, Fiji/ImageJ, CloudVolume, TensorStore, Zarr, N5, DVID, CAVE, or related tools. * Affinity prediction, watershed segmentation, flood filling networks, U-Nets, transformers for vision, or other computer vision models for biological image data. * Distributed data processing, cloud infrastructure, GPU inference, and high-throughput ML pipelines. * GPU kernel development experience is a definite plus. * Large-scale n-dimensional array processing in Python, C++, Java, or similar environments. * Strong software engineering skills, including clean code, version control, testing, documentation, and reproducible workflows. * Experience with large data systems, ideally at TB scale or above. * Experience with computer vision, biological image segmentation, or volumetric data analysis. * Strong communication skills and ability to collaborate with neuroscientists, microscopists, ML engineers, and data infrastructure engineers. Representative Projects * Building Eon's large-scale connectomics segmentation and proofreading pipeline. * Creating efficient workflows for affinity prediction, watershed segmentation, synapse detection, and neuron reconstruction. * Developing Neuroglancer-style visualization infrastructure for large expanded-brain datasets. ## Description We 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. This 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. This is a hands-on role for someone who is comfortable moving between ML experimentation, production data infrastructure, scientific computing, and computational neuroscience., * Build, optimize, and maintain large-scale connectomics data pipelines for volumetric microscopy data. * Develop and improve machine learning workflows for image segmentation, affinity prediction, watershed/post-processing, synapse detection, and neural reconstruction. * Work with large-scale n-dimensional image data, including TB- to PB-scale datasets. * Run controlled ML experiments to improve segmentation accuracy, throughput, and reliability. * Create polished, compelling visualizations of connectomic data, neural activity, and reconstructed circuits. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Making neural networks portable with ONNX](https://www.wearedevelopers.com/videos/301-making-neural-networks-portable-with-onnx) - [An Applied Introduction to eBPF with Go](https://www.wearedevelopers.com/videos/1075-an-applied-introduction-to-ebpf-with-go) - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Into the hive of eBPF!](https://www.wearedevelopers.com/videos/1199-into-the-hive-of-ebpf) ## Related Articles - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Dev Digest 129 - Now that's what I call private data!](https://www.wearedevelopers.com/magazine/468-dev-digest-129-now-that-s-what-i-call-private-data) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)