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$49.5K

$92K

$142.5K

How much do microscopy ai jobs pay per year?

As of Sep 9, 2026, the average yearly pay for microscopy ai in the United States is $92,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $74,500.00 and $103,000.00 per year, depending on experience, location, and employer.

What is Microscopy AI?

Microscopy AI refers to the use of artificial intelligence technologies in microscopy to automate image analysis, improve image quality, and extract meaningful insights from microscopic data. By leveraging machine learning and deep learning algorithms, AI can identify patterns, classify cells or structures, and even predict outcomes in biological and material samples. This enhances the efficiency and accuracy of research in fields like biology, medicine, and materials science. Microscopy AI tools are increasingly being integrated into both research and clinical workflows to accelerate discoveries and improve diagnostics.

How do professionals in Microscopy AI typically collaborate with scientists and engineers during research projects?

Microscopy AI professionals frequently work alongside biologists, chemists, and engineers to develop and refine AI-driven image analysis tools. They participate in interdisciplinary meetings to interpret data requirements and tailor algorithms to specific research objectives. Open communication is essential, as team members rely on Microscopy AI experts to translate complex AI models into practical solutions for imaging challenges. This collaborative environment fosters innovation and allows for continuous learning from diverse scientific perspectives.

What are the key skills and qualifications needed to thrive as a Microscopy AI specialist, and why are they important?

To thrive as a Microscopy AI Specialist, you need a solid background in microscopy techniques, image analysis, and a strong foundation in computer science or data science, often supported by an advanced degree in a related field. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), image processing software, and programming languages like Python is essential. Strong problem-solving abilities, attention to detail, and effective communication are vital soft skills in this interdisciplinary role. These skills ensure the development and deployment of accurate, innovative AI solutions that advance scientific research and imaging applications.

What is the difference between Microscopy Ai vs Microscopy Technician?

AspectMicroscopy AiMicroscopy Technician
Required CredentialsTypically requires a background in AI, computer science, or related fields; may have certifications in AI or data analysisUsually requires an associate's or bachelor's degree in microscopy, biology, or related sciences; certifications in microscopy techniques are common
Work EnvironmentPrimarily office or lab settings involving data analysis, software operation, and AI model developmentLaboratory settings focused on sample preparation, microscopy operation, and image capturing
Employer & Industry UsageUsed in research institutions, biotech companies, and medical labs integrating AI for image analysisEmployed in hospitals, research labs, and manufacturing facilities performing microscopy tasks

While Microscopy Ai focuses on developing and applying AI algorithms to analyze microscopy images, Microscopy Technicians operate microscopes and prepare samples. Both roles are essential in labs but differ in skills, responsibilities, and work focus.

What other helpful pages are available for Microscopy Ai?

Other pages related to Microscopy Ai:

Infographic showing various Microscopy Ai job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 76% Full Time, 19% Part Time, and 4% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $92,018 per year, or $44.2 per hour.

Machine Learning Scientist I/II, Multi-Modal Scientific Reasonings

Cambridge, MA • On-site

Lila Sciences
See more jobs at Lila Sciencesexternal

Full-time

This job post has expired 2 days ago. Applications are no longer accepted.

Job description

Your Impact at LILA

We're hiring a Machine Learning Scientist to advance multimodal reasoning with visionlanguage models (VLMs) on real-world scientific data including, but not limited to: figures and plots, microscopy data from diverse sources. You'll design and build stateoftheart methods to advance the state of Scientific Superintelligence.

What You'll Be Building

  • Lead research on multimodal reasoning systems that interpret scientific data (images, plots, text, etc) using stateoftheart and custom VLMs.
  • Design training, adaptation and test-time methods and strategies (e.g., instruction tuning, supervised learning, RLHF, RAG) for scientific understanding tasks.
  • Build datasets and benchmarks from real scientific artifacts (e.g., microscopy, spectra, protocols) to understand model performance.
  • Develop perception modules (e.g, OCR, table/structure recognition, plot parsing) for multi-modal data modalities.
  • Collaborate with domain scientists and engineers to scale research into production ready systems for scientific superintelligence.

What You'll Need to Succeed

  • Advanced degree in a relevant field (CS/AI, Applied Math/Stats, EE) or a physicalsciences discipline (Materials, Chemistry, Physics) with strong ML focus; or equivalent research/industry experience.
  • Track record in multimodal ML or VLMs demonstrated via shipped systems, publications, or opensource.
  • Understanding of scientific QA/benchmarks and custom evaluation design.
  • Experience with multi-modal fine-tuning, document parsing & understanding, dataset curation and benchmarking.
  • Strong engineering skills centered on modern machine learning frameworks (e.g., PyTorch, Huggingface).
  • Clear communication and collaboration in crossfunctional settings.

Bonus Points For

  • Experience with scientific data modalities in real-world laboratories such as microscopy images.
  • Publications in top ML/CV/NLP venues or tangible impact in applied industrial research.
  • Contributions to opensource multimodal tooling, evaluation suites, or datasets.

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