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Temporary Remote Image Segmentation Jobs in Chicago, IL

Career Renew

Senior Machine Learning Engineer

Chicago, IL · Remote

$165K - $225K/yr

... remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus ... Vision Transformers, Diffusion Models, GANs, semantic segmentation, or classical image enhancement ...

Blue Wheel Media

Paid Search Strategist (REMOTE)

Chicago, IL · Remote

$84K - $97K/yr

Proactively identify growth opportunities, such as new audience segments, emerging platforms, and ... Coordinate with creative teams to develop and deploy compelling ad assets, including image, video ...

Blue Wheel Media

Paid Search Strategist (REMOTE)

Chicago, IL · Remote

$83K - $96K/yr

Proactively identify growth opportunities, such as new audience segments, emerging platforms, and ... Coordinate with creative teams to develop and deploy compelling ad assets, including image, video ...

Blue Wheel Media

Paid Search Strategist (REMOTE)

Chicago, IL · On-site +1

$84K - $97K/yr

Proactively identify growth opportunities, such as new audience segments, emerging platforms, and ... Coordinate with creative teams to develop and deploy compelling ad assets, including image, video ...

Aquent

Paid Social Designer

Chicago, IL · On-site +1

$42 - $47/hr

Temporary Salary: $42-47 Hourly Start Date: Jul 20, 2026 Work will be primarily remote. However ... Ability to develop creative briefs, mood and image boards * Ability to strategize and lead ideation ...

All JobsImage Segmentation JobsTemporary Remote Image Segmentation JobsTemporary Remote Image Segmentation Jobs in Chicago, IL

Temporary Remote Image Segmentation information

See Chicago, IL salary details

$19

$44

$72

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

As of Jul 26, 2026, the average hourly pay for temporary remote image segmentation in Chicago, IL is $44.92, according to ZipRecruiter salary data. Most workers in this role earn between $34.90 and $52.26 per hour, depending on experience, location, and employer.

What is the difference between Temporary Remote Image Segmentation vs Image Annotation Specialist?

AspectTemporary Remote Image SegmentationImage Annotation Specialist
Primary FocusSegmenting images to identify specific regions or objectsLabeling and annotating images for training AI models
Skills & CertificationsImage processing, computer vision, basic codingData labeling, attention to detail, familiarity with annotation tools
Work EnvironmentRemote, project-based, often short-termRemote or on-site, project-based or ongoing
Industry UsageAI development, computer vision projectsMachine learning, AI training datasets

Temporary Remote Image Segmentation involves isolating specific regions within images for computer vision tasks, often requiring technical skills in image processing. In contrast, Image Annotation Specialists focus on labeling images to create training data for AI models. Both roles are remote, project-based, and essential in AI development, but they differ in technical complexity and specific tasks.

What are popular job titles related to Temporary Remote Image Segmentation jobs in Chicago, IL? For Temporary Remote Image Segmentation jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Temporary Remote Image Segmentation jobs in Chicago, IL look for? The top searched job categories for Temporary Remote Image Segmentation jobs in Chicago, IL are:
Temporary Remote Image Segmentation jobs near you

Senior Machine Learning Engineer

Career Renew

Chicago, IL • Remote

$165K - $225K/yr

Full-time

Posted 3 days ago

Job description

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a fully remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus equity.
We are the leading virtual staining company revolutionizing digital pathology adoption worldwide through cutting-edge AI-powered technology. Our solutions deliver diagnostic-quality results in minutes while preserving tissue samples for comprehensive analysis.
Our breakthrough DeepStain™ and ReStain™ technologies enable unlimited virtual staining from a single tissue sample, eliminating the bottlenecks and limitations of traditional chemical staining processes. This innovation supports the critical evolution from research applications to clinical deployment, empowering laboratories to advance their digital pathology capabilities while reducing chemical waste, improving operational efficiency, and expanding diagnostic possibilities.

About the Role

We are seeking an experienced Senior ML Engineer to join our team who owns the representation-learning and generative modeling stack that powers Pictor’s virtual staining. The ideal candidate will have deep expertise in Machine Learning and building generalizable, production-ready models, and evaluations that stand up in clinical workflows.
Design and implement novel computer vision and deep learning algorithms for virtual staining and digital pathology applications
Conduct rigorous experiments to evaluate algorithm performance, validate research hypotheses, and drive iterative improvements
Develop and advance ML models leveraging Vision Transformers, Diffusion Models, GANs, and generative architectures for image-to-image translation tasks
Apply classical and learned image enhancement, denoising, and semantic segmentation techniques to histopathology imaging challenges
Explore image representation in latent space for efficient, high-fidelity virtual staining
Stay current with state-of-the-art research, identifying opportunities to apply novel techniques to PictorLabs’ product roadmap

Collaboration
Collaborate with ML Engineering and software teams to translate research prototypes into production-ready systems meeting latency and throughput requirements
Work with large-scale pathology datasets to train, validate, and fine-tune foundation models and custom architectures
Partner with software engineers, data scientists, and pathology domain experts to integrate research into production systems
Contribute to best practices for data engineering, data governance, and data quality across research and production pipelines
Leverage AI coding and ideation tools to accelerate research velocity and prototype new approaches

Required Qualifications

PhD (preferred) or Master’s degree in Computer Science, Electrical Engineering, or a related field
Deep expertise in computer vision and deep learning, with hands-on experience in one or more of: Vision Transformers, Diffusion Models, GANs, semantic segmentation, or classical image enhancement and denoising
Expert proficiency in Python and PyTorch and other scientific computing environments a plus
Strong mathematical foundation in linear algebra, probability, and optimization
Experience with large-scale model training, distributed computing, or cloud ML infrastructure (AWS, GCP, or Azure)
Knowledge of handling large scale image data, data version controls, model registry, has experience dealing with ML lifecycles
Experience with feature search, data balancing, and data curation pipelines.
Knowledge of software engineering best practices including version control (Git) and CI/CD pipelines
Excellent collaboration and communication skills, with the ability to work effectively in a fast-paced, cross-functional international startup environment
Extensive use of AI tools for coding, optimization, and ideation

Preferred Qualifications

Experience with medical imaging, digital pathology, or whole slide image (WSI) processing
Experience with LoRAs, transformer architecture and state of the art image to image translation models (Flux 2, Z-Image) and the Hugging face ecosystem
Background in generative models and fine-tuning of foundation models
Experience with GPU acceleration and optimization, including CUDA kernel engineering, TensorRT/ONNX export, and inference serving frameworks such as Triton
Experience with hosting computer vision model inference on NVIDIA DGX Spark.
Understanding of FDA regulatory requirements for AI/ML in medical devices
Experience with MLOps tools (MLflow, Kubeflow) and model versioning practices
Develop tools and frameworks to streamline ML research workflows, experimentation, and reproducibility

What We Offer

The opportunity to work on technology that directly improves patient outcomes and transforms clinical diagnostics, alongside a talented team of engineers and researchers pushing the boundaries of AI in healthcare. You will have the freedom to pursue high-impact research while seeing your work deployed at scale in real clinical environments.