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Flexible Remote Image Segmentation Jobs (NOW HIRING)

Weekday AI

Computer Vision Expert

$80 - $110/hr

This is a part-time, fully remote opportunity requiring approximately 20 hours per week ... Image Segmentation * Object Recognition * Vision-Language & Multimodal Reasoning * Image and Video ...

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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 Sep 12, 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 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 are the key skills and qualifications needed to thrive as a flexible remote image segmentation specialist?

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 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.

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Flexible Remote Image Segmentation jobs near you
Infographic showing various Flexible Remote Image Segmentation job openings in the United States as of September 2026, with employment types broken down into 87% Full Time, and 13% Part Time. Highlights an 13% In-person, and 87% Remote job distribution, with an average salary of $90,701 per year, or $43.6 per hour.

Senior Computer Vision Engineer ID72408

Baltimore, MD • On-site, Remote

AgileEngine
Software Development • 201 - 500 employees
Learn more about AgileEngineexternal

$103K - $142K/yr

Full-time

Posted 20 days ago

Job description

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE
We are looking for a Senior Computer Vision Engineer to own applied model development across computer vision, machine learning, and data science use cases — building and deploying solutions for object detection, image segmentation, classification, and video analysis. You will evaluate and fine-tune model architectures using PyTorch and TensorFlow, build broader ML models for forecasting and anomaly detection, and apply practical knowledge of computer vision hardware including cameras, sensors, and edge devices.

WHAT YOU WILL DO
- Own the applied model development process across computer vision, AI/ML, and broader data science use cases;
- Translate complex business problems into viable, practical, and scalable AI/ML solutions;
- Evaluate various model options, train and fine-tune selected architectures, and rigorously analyze model performance;
- Develop and deploy solutions for object detection, image segmentation, image classification, and video analysis;
- Build and maintain models for time-series forecasting, anomaly detection, regression, clustering, and general data analysis;
- Apply practical knowledge of real-world constraints—such as lighting, sensor limitations, and edge device compute power—to ensure optimal data quality and robust model performance in production.

MUST HAVES
- You must be authorized to work for ANY employer in the US (e.g., Green card holders, TN visa holders, GC EAD, H4 EAD, U4U with EAD), as we are unable to sponsor or take over employment visa sponsorship at this time;
- 3 to 5 years of professional experience in Computer Vision, Machine Learning, Data Science, or a related field;
- Degree in Computer Science, Engineering, Data Science, Mathematics, or a related discipline (or equivalent practical experience);
- Engineers located in the US must reside in Dallas, TX, and be open to working from the office (onsite);
- Strong, production-level proficiency in Python;
- Deep hands-on experience with PyTorch and/or TensorFlow;
- Proven track record of building and deploying models for detection, segmentation, classification, and image/video analysis;
- Solid understanding of broader ML and data science techniques (time-series modeling, forecasting, anomaly detection, regression, and clustering);
- Practical experience working with computer vision hardware, including cameras, sensors, and lighting setups;
- Familiarity with deploying models on edge devices;
- Strong understanding of how physical and real-world constraints impact data quality, model training, and inference;
- Upper-intermediate English level.

PERKS AND BENEFITS
- Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
- Exciting projects: Modern solutions with Fortune 500 and top product companies.
- Flextime: Flexible schedule with remote and office options.