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Flexible Remote Image Segmentation Jobs (NOW HIRING)

digitalglobalservices

Computer Vision Engineer

Little Rock, AR · On-site +1

$106.90K - $126.10K/yr

Remote / On-site / Flexible Employment Type: Full-time Job Summary We are seeking a skilled ... image segmentation, and pose estimation. Train and optimize deep learning models using frameworks ...

Danaher

Senior Software Engineer - USA Remote

OR · Remote

$122.40K - $161.30K/yr

Our advanced microscopes and AI-based image analysis solutions enable users to gain profound ... We recognize the benefits of flexible, remote working arrangements for eligible roles and are ...

Danaher

Senior Software Engineer - USA Remote

Raleigh, NC · Remote

$119.10K - $157K/yr

Our advanced microscopes and AI-based image analysis solutions enable users to gain profound ... We recognize the benefits of flexible, remote working arrangements for eligible roles and are ...

Danaher

Senior Data Scientist - USA Remote

OR · On-site +1

... AI, and image AI solutions. In this role, you will help build multimodal datasets, develop ... We recognize the benefits of flexible, remote working arrangements for eligible roles and are ...

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Flexible Remote Image Segmentation information

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$18

$43

$70

How much do flexible remote image segmentation jobs pay per hour?

As of May 31, 2026, the average hourly pay for flexible remote image segmentation in the United States is $43.61, according to ZipRecruiter salary data. Most workers in this role earn between $33.89 and $50.72 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Flexible Remote Image Segmentation Specialist, and why are they important?

To thrive as a Flexible Remote Image Segmentation Specialist, you need a strong background in computer vision, image processing, and data annotation, typically supported by a degree in computer science or a related field. Familiarity with tools like Python, OpenCV, TensorFlow, and specialized annotation platforms, along with experience in using cloud-based collaboration systems, is essential. Attention to detail, strong time management, and effective remote communication are vital soft skills for success in this role. These abilities ensure high-quality, accurate segmentation results and smooth collaboration in distributed teams, which is crucial for delivering reliable data for machine learning projects.

What are some common challenges faced in a flexible remote image segmentation role, and how can they be addressed?

In a flexible remote image segmentation role, one common challenge is maintaining clear communication with team members, especially when collaborating across different time zones. Another challenge is ensuring consistent annotation quality, as image segmentation tasks require precision and attention to detail. To address these, it's helpful to use collaborative tools, establish clear guidelines, participate in regular team check-ins, and seek feedback on your work. Setting up a dedicated workspace and sticking to a structured routine can also enhance focus and productivity.

What is Flexible Remote Image Segmentation?

Flexible Remote Image Segmentation refers to the process of identifying and separating different objects or regions within digital images using specialized software, while working remotely with flexible hours. Professionals in this role use machine learning and image processing techniques to annotate, label, or segment images for applications such as medical imaging, self-driving cars, or AI training datasets. The 'flexible remote' aspect means workers can complete tasks from any location and often on their own schedule, making it ideal for those seeking work-life balance or part-time opportunities.

What is the difference between Flexible Remote Image Segmentation vs Flexible Remote Data Annotation?

AspectFlexible Remote Image SegmentationFlexible Remote Data Annotation
Primary FocusDividing images into meaningful segments for analysisLabeling and annotating data, including images, for machine learning
Skills RequiredImage processing, computer vision, annotation toolsAttention to detail, labeling accuracy, annotation tools
Work EnvironmentRemote, often collaborative with AI teamsRemote, often collaborative with data science teams
Industry UsageComputer vision, autonomous vehicles, medical imagingMachine learning, AI training, data management

While both roles involve working with data and images remotely, Flexible Remote Image Segmentation focuses on dividing images into segments for analysis, whereas Flexible Remote Data Annotation involves labeling data to train AI models. Understanding these differences helps in choosing the right role based on skills and industry needs.

More about Flexible Remote Image Segmentation jobs
What cities are hiring for Flexible Remote Image Segmentation jobs? Cities with the most Flexible Remote Image Segmentation job openings:
What are the most commonly searched types of Remote Image Segmentation jobs? The most popular types of Remote Image Segmentation jobs are:
What states have the most Flexible Remote Image Segmentation jobs? States with the most job openings for Flexible Remote Image Segmentation jobs include:
What job categories do people searching Flexible Remote Image Segmentation jobs look for? The top searched job categories for Flexible Remote Image Segmentation jobs are:
Flexible Remote Image Segmentation jobs near you
Infographic showing various Flexible Remote Image Segmentation job openings in the United States as of May 2026, with employment types broken down into 76% Full Time, and 24% Part Time. Highlights an 6% In-person, and 94% Remote job distribution, with an average salary of $90,701 per year, or $43.6 per hour.

Radiologic Technologist | Remote

Crossing Hurdles

Manhattan, NY • Remote

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Lead the full lifecycle of 3D Slicer projects, from raw imaging import to final processed outputs. Perform DICOM import, image segmentation, structure delineation, and advanced 3D reconstructions using 3D Slicer. Execute image registration, quantitative analysis, and troubleshooting within the 3D Slicer environment.

Document and clearly explain workflows, clinical rationale, and technical decisions in written and verbal formats. Collaborate remotely with multidisciplinary teams across radiology, neuroscience, or biomedical engineering. Provide domain expertise to help train AI systems on expert 3D Slicer usage and decision-making.

Identify and resolve software issues to ensure smooth end-to-end project delivery. Requirements Demonstrated professional expertise in 3D Slicer with end-to-end project experience. Strong background in medical imaging, radiology, neuroscience, surgical planning, or related field.

Deep experience with DICOM workflows, segmentation, registration, and 3D visualization. Excellent written and verbal communication skills. Hands-on familiarity with 3D Slicer modules, extensions, and troubleshooting.

Proven ability to independently deliver high-quality projects. Comfortable working remotely with cross-functional teams. #J-18808-Ljbffr

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