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From Home Image Segmentation Jobs in California (NOW HIRING)

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From Home Image Segmentation information

What is the difference between From Home Image Segmentation vs From Home Data Annotation?

AspectFrom Home Image SegmentationFrom Home Data Annotation
Primary FocusSegmenting images into meaningful partsLabeling data points or objects in images
Required SkillsComputer vision, image processing, annotation toolsData labeling, attention to detail, annotation software
Work EnvironmentRemote, flexible, tech-focusedRemote, flexible, data-driven
Industry UsageAI, machine learning, autonomous vehiclesAI, machine learning, data training

From Home Image Segmentation involves dividing images into segments for AI training, requiring technical skills in image processing. From Home Data Annotation focuses on labeling data for machine learning, emphasizing accuracy in data labeling. Both roles are remote, industry-specific, and essential for AI development, but they differ in technical complexity and focus areas.

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From Home Image Segmentation jobs near you

Machine Learning Engineer, Connectomics

Eonsystems

San Francisco, CA • On-site

$140 - $190/hr

Other

Posted 15 days ago

Job description

About Us

Eon 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.

We 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.

Role

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.

Responsibilities
  • 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.
Skills
  • 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.
Salary

Competitive salaries, including equity, apply.

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