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Remote Medical Image Deep Learning Jobs (NOW HIRING)

Terra AI

Senior/Staff Machine Learning Researcher

By combining advanced machine learning, probabilistic modeling, and deep geoscience expertise ... Role description In the same way image generators have shown the remarkable ability to produce a ...

Natera

Machine Learning Scientist, Multimodal AI

$124K - $171K/yr

This role develops and deploys deep learning models across digital pathology, genomics ... Remote USA $124,800-$171,600 USD OUR OPPORTUNITY Natera™ is a global leader in cell-free DNA ...

The Pennsylvania State University

Approval of remote and hybrid work is not guaranteed regardless of work location.For additional ... Build andoptimizepredictive and generative models (e.g., deep learning, probabilistic models ...

Harvard University

Machine Learning Engineer

Boston, MA · On-site +1

... Python deep learning software stack, particularly expertise in PyTorch, Numpy, and related packages. * Experience handling and processing large and diverse datasets, especially medical texts ...

Beacon Biosignals

Algorithm Engineer

Boston, MA · On-site +1

Enhance our internal deep learning and machine learning tools to boost team efficiency, introduce ... You are familiar with biosignals, medical imaging data, or large time-series datasets, or are ...

Beacon Biosignals

Algorithm Engineer

Boston, MA · On-site +1

Enhance our internal deep learning and machine learning tools to boost team efficiency, introduce ... You are familiar with biosignals, medical imaging data, or large time-series datasets, or are ...

Overstory

Strong background in deep learning, computer vision, or remote sensing * Skilled in designing end-to-end ML systems - from data ingestion and preprocessing to deployment and monitoring * Hands-on ...

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Remote Medical Image Deep Learning information

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How much do remote medical image deep learning jobs pay per hour?

As of Jul 18, 2026, the average hourly pay for remote medical image deep learning in the United States is $21.50, according to ZipRecruiter salary data. Most workers in this role earn between $18.03 and $22.84 per hour, depending on experience, location, and employer.

What is the difference between Remote Medical Image Deep Learning vs Remote Medical Imaging Technician?

AspectRemote Medical Image Deep LearningRemote Medical Imaging Technician
CredentialsBackground in AI, machine learning, or computer science; often requires advanced degreesCertification in radiologic technology or imaging; typically requires licensing
Work EnvironmentPrimarily remote, focused on data analysis and model developmentRemote or on-site, involved in image acquisition and processing
Industry UsageUsed in healthcare AI development, research, and diagnostics supportUsed in hospitals, clinics, and imaging centers for patient care

Remote Medical Image Deep Learning focuses on developing AI models to analyze medical images, requiring expertise in AI and data science. In contrast, Remote Medical Imaging Technicians handle image acquisition and processing, often with clinical certifications. Both roles are vital in healthcare but differ in skills, responsibilities, and work environment.

More about Remote Medical Image Deep Learning jobs
What cities are hiring for Remote Medical Image Deep Learning jobs? Cities with the most Remote Medical Image Deep Learning job openings:
What are the most commonly searched types of Medical Image Deep Learning jobs? The most popular types of Medical Image Deep Learning jobs are:
What states have the most Remote Medical Image Deep Learning jobs? States with the most job openings for Remote Medical Image Deep Learning jobs include:
What job categories do people searching Remote Medical Image Deep Learning jobs look for? The top searched job categories for Remote Medical Image Deep Learning jobs are:
Remote Medical Image Deep Learning jobs near you
Infographic showing various Remote Medical Image Deep Learning job openings in the United States as of July 2026, with employment types broken down into 73% Full Time, 25% Part Time, and 2% Contract. Highlights an 72% Physical, 2% Hybrid, and 26% Remote job distribution, with an average salary of $44,724 per year, or $21.5 per hour.

Senior/Staff Machine Learning Researcher

Terra AI

Remote

Full-time

Re-posted 4 days ago

Job description

Terra AI is building a new category at the intersection of artificial intelligence, geoscience, and critical resource development.
As global demand for copper, lithium, nickel, rare earth elements, geothermal energy, and other strategic resources accelerates, the mining and subsurface industries face a growing challenge: traditional exploration methods remain slow, expensive, and highly uncertain. Terra AI was founded to help solve this problem by redefining how critical resources are discovered, evaluated, and developed.
By combining advanced machine learning, probabilistic modeling, and deep geoscience expertise, Terra AI helps exploration and mining companies make faster, more informed subsurface decisions with greater confidence and capital efficiency. The company's platform integrates geological, geophysical, and drilling data into intelligent systems designed to improve targeting accuracy, accelerate discovery timelines, and reduce exploration risk.
Backed by leading investors including Khosla Ventures and working alongside strategic industry partners including Rio Tinto, Ero Copper, and Ramaco Resources, Terra AI is emerging as one of the more closely watched AI-native companies operating within the mining and critical minerals sector.
Terra AI's mission is to define the new global standard for data-driven critical resource development - breaking the cost and time curve required to support electrification, energy security, and the global energy transition.
The company operates with a strong partnership mentality, combining technical rigor, candid communication, continual learning, and environmental stewardship to help modern exploration teams solve some of the world's most important resource challenges.
Role description
In the same way image generators have shown the remarkable ability to produce a diverse set of realistic pictures conditioned on a text prompt (and other inputs), we are developing a generative model that produces 3D geological models conditioned on geophysical surveys, borehole measurements, and other forms of physical observation. The outputs of the generative model capture what we know and don't know about the state of the subsurface, allowing explorers to make maximally informed decisions about how and where to explore for critical resources.
We are looking for a talented deep learning engineer or scientist to lead the development of this model that will revolutionize decision-making in the earth subsurface for a wide range of clean energy applications.
Role Responsibilities
  • Design, train, test, and iterate on diffusion models for 3D geological models
  • Design, train, test, and iterate on an approach for conditioning generation on geophysical data and other observations
  • Inform the generation of synthetic data to improve model performance
  • Adapt diffusion modeling approach to specific real-world projects in collaboration with project teams.

Qualifications
Required Qualifications:
  • Extensive PyTorch Experience
    • Deep understanding of PyTorch, including writing custom modules, optimizing training, and debugging issues in large-scale models.
  • Expertise in Developing Large Deep Learning Models from Scratch
    • Proven ability to design, implement, and train complex deep learning architectures from the ground up.
  • Data Curation Skills
    • Hands-on experience in creating, cleaning, and maintaining high-quality datasets tailored for machine learning applications.
  • Strong Software Engineering and Design Experience
    • Proficient in software development best practices, including version control, testing, and code optimization.
    • Familiarity with designing scalable and maintainable systems.

Nice-to-haves:
  • Experience with Generative Models
    • Familiarity with generative architectures, particularly diffusion models, and an emphasis on posterior sampling methods.
  • Knowledge of Transformer Architectures
    • Experience building and training transformers, especially in applications involving 3D data.
  • Scaling Models Across Large GPU Clusters
    • Expertise in parallelizing models across multiple GPUs and optimizing distributed training pipelines.
  • Cloud Infrastructure Expertise
    • Experience setting up, managing, and optimizing cloud environments for machine learning workloads, including provisioning resources and managing costs.

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