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Image Segmentation Jobs (NOW HIRING)

Experience with image segmentation, particle finding, feature detection, or morphology analysis. * Interest in building practical autonomy for scientific instruments across real sample types and ...

... and/or image segmentation, and/or computer vision programing in matlab, and/or C/C++/C#, and/or Java, Qualifications Possesses educational qualifications and work experience as established by ...

Staff AI/ML Engineer

Westford, MA · On-site

$99K - $198K/yr

Design, develop and deploy machine learning and deep learning models for clinical applications including image segmentation, feature detection, classification and quantitative analysis of OCT images.

New

Staff AI/ML Engineer

Westford, MA · On-site

$99K - $198K/yr

Design, develop and deploy machine learning and deep learning models for clinical applications including image segmentation, feature detection, classification and quantitative analysis of OCT images.

New

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Image Segmentation information

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$18

$43

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How much do image segmentation jobs pay per hour?

As of Jul 11, 2026, the average hourly pay for 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 common challenges faced in an Image Segmentation role?

Professionals in image segmentation often encounter challenges such as dealing with low-quality or ambiguous images, managing large datasets, and ensuring consistency in labeling across various data sources. Staying up to date with rapidly evolving algorithms and tools in computer vision is also important for maintaining best practices. Effective communication with data scientists, engineers, and project managers is key for understanding project requirements and delivering high-quality segmentation results. Overcoming these challenges not only helps produce more accurate models but also contributes to personal skill growth and deeper team collaboration.

What are the key skills and qualifications needed to thrive in the Image Segmentation position, and why are they important?

To excel in Image Segmentation, you need a solid background in computer vision, machine learning, and data annotation, often supported by a degree in computer science or a related field. Familiarity with tools such as Python, OpenCV, TensorFlow, and specialized annotation software is typically required. Attention to detail, collaborative mindset, and effective communication are important soft skills in this position. Mastering these abilities ensures precise segmentation work, efficient teamwork, and contributions to projects in fields such as healthcare, autonomous driving, and digital imaging.

What is an Image Segmentation job?

An Image Segmentation job involves developing algorithms and models to partition digital images into meaningful regions or objects. Professionals in this role often work with computer vision, deep learning, and artificial intelligence to improve image analysis. Tasks may include data annotation, training machine learning models, and optimizing segmentation accuracy for applications like medical imaging, autonomous vehicles, and industrial automation. Strong programming skills in Python and experience with frameworks like TensorFlow or OpenCV are typically required.

More about Image Segmentation jobs
What cities are hiring for Image Segmentation jobs? Cities with the most Image Segmentation job openings:
What are the most commonly searched types of Image Segmentation jobs? The most popular types of Image Segmentation jobs are:
What states have the most Image Segmentation jobs? States with the most job openings for Image Segmentation jobs include:
Infographic showing various Image Segmentation job openings in the United States as of July 2026, with employment types broken down into 50% Full Time, and 50% Part Time. Highlights an 75% In-person, and 25% Hybrid job distribution, with an average salary of $90,701 per year, or $43.6 per hour.
Data Scientist, AI for Biomedical Imaging

Data Scientist, AI for Biomedical Imaging

Novartis

Cambridge, MA

Full-time

Medical, Life, Retirement, PTO

Posted 2 days ago

New


Novartis rating

7.5

Company rating: 7.5 out of 10

Based on 9 frontline employees who took The Breakroom Quiz

55th of 74 rated pharmaceutical


Job description

Job Description Summary

The mission of Novartis is to reimagine medicine, and our team advances that mission by applying advanced image analysis, computer vision, and AI methods to early drug discovery. We partner closely with experimental scientists, disease-area teams, data scientists, bioinformatics experts, and platform engineers to extract meaningful biological insight across diverse imaging modalities (high-content screening, custom microscopy platforms) and biological model systems (cellular assays, co-cultures, organoids, tissue models).
To grow this capability, we are seeking a seasoned, innovative, and collaborative data scientist with deep expertise in AI-enabled image analysis to join the Data Science team in Discovery Sciences (DSc) at Novartis Biomedical Research, Cambridge, MA. This role combines hands-on delivery of robust image analysis workflows with advanced AI method development, including biomedical image segmentation, representation learning, foundation models, and scalable deployment. The successful candidate will embed within the research community as the team's scientific lead for imaging-AI, partnering directly with wet-lab scientists to translate complex biological questions into rigorous, reproducible, and impactful analysis strategies.


Job Description

Internal Job Title: Senior Expert I/II, Data Science

Position Location: Onsite, Cambridge, MA #LI-Onsite

Role Responsibilities:

  • Lead AI-enabled image analysis strategies for complex biological imaging workflows, acting as the embedded imaging-AI scientific partner working side-by-side with wet-lab scientists to understand emerging assay needs, align approaches with scientific priorities and platform standards,and explain advanced AI concepts in accessible terms.

  • Identify high-impact opportunities where AI can deliver meaningful scientific value and define rigorous benchmarking and evaluation strategies to guide method selection.

  • Develop, validate, and deploy robust image analysis algorithms to characterize cellular, organoid, tissue, and other complex biological phenotypes in high-throughput and high-content imaging data, generating reproducible outputs that support decision-making in drug discovery projects.

  • Drive adoption of advanced AI methods for imaging, including deep learning, vision foundation models, embedding-based phenotyping, segmentation, classification, and multimodal integration, translating state-of-the-art methods into practical, validated workflows that augment expert review and enable scalable interpretation of large, high-dimensional datasets.

  • Contribute to scalable, reusable image analysis workflows in partnership with other data scientists, data engineering, and platform teams, championing best practices across the workflow lifecycle.

Essential Requirements:

  • PhD in computer science, AI, machine learning, biomedical image analysis, computational imaging, data science, or a related quantitative field, with 3+ years of applied experience in AI for bioimaging and computer vision.

  • Demonstrated experience developing and validating image analysis algorithms for biological, biomedical, or pharmaceutical research applications, with practical experience in image segmentation, feature extraction, phenotypic profiling, object classification, or representation learning applied to high-content or high-throughput imaging data.

  • Practical expertise in designing benchmarking and evaluation strategies to compare image analysis methods and guide rigorous, evidence-based model selection.

  • Ability to work effectively in Linux-based high-performance computing, cloud, or large-scale data processing environments, with a strong commitment to reproducible research, version control, testing, and data provenance.

  • Strong proficiency in Python and the scientific deep learning stack (e.g., PyTorch, Hugging Face, Lightning, MONAI), along with hands-on experience using image analysis tools such as scikit-image, OpenCV, napari, Cellpose, StarDist, InstanSeg, and OME-Zarr.

  • Self-motivated experienced contributor who thrives in a collaborative, multidisciplinary environment with biologists, imaging scientists, software engineers, and bioinformatics partners, working with appropriate independence and helping shape project direction through both technical expertise and scientific judgment.

  • Excellent scientific communication and stakeholder engagement skills, including the ability to explain complex AI and image analysis concepts to experimental scientists, project teams, engineers, and non-technical audiences.

Desirable Requirements:

  • Experience developing or adapting foundation models, self-supervised learning approaches, multimodal AI models, or embedding-based analysis methods (e.g., DINO, CLIP, SAM) for biological imaging data.

  • Familiarity with the drug discovery pipeline, phenotypic screening, translational biology models, or pharmaceutical research processes.

  • Demonstrated success in turning project-specific solutions into reusable, scalable workflows or standardized analysis products integrated into enterprise platforms, production pipelines, or user-facing tools.

  • Track record of scientific publication, conference presentations, open-source contributions, or internal technical leadership in AI, computer vision, biomedical image analysis, or related fields.

  • Familiarity with agentic coding tools and AI-assisted development workflows (e.g., Claude Code, Copilot).

Compensation & Benefits:

The salary for this position is expected to range between $126,000 and $234,000 USD annually for Senior Expert I, Data Science, and $138,600 and $257,400 USD annually for Senior Expert II, Data Science. The final salary offered is determined based on factors like, but not limited to, relevant skills andexperience, and upon joining Novartis will be reviewed periodically. Novartis may change the publishedsalary range based on company and market factors.


Your compensation will include a performance-based cash incentive and, depending on the level of therole, eligibility to be considered for annual equity awards.


US-based eligible employees will receive a comprehensive benefits package that includes health, life anddisability benefits, a 401(k) with company contribution and match, and a variety of other benefits. Inaddition, employees are eligible for a generous time off package including vacation, personal days,holidays and other leaves.


To learn more about the culture, rewards and benefits we offer our people click here.


EEO Statement:

The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status.


Accessibility and reasonable accommodations

The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or to perform the essential functions of a position, please send an e-mail to us.reasonableaccommodations@novartis.com or call +1(877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.


Salary Range

$126,000.00 - $234,000.00


Skills Desired

Artificial Intelligence (AI), Biostatistics, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis

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