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Remote Healthcare Machine Learning Jobs (NOW HIRING)

Pelica Health

Machine Learning Engineer

Pelica Health is an innovative company focused on value-based care, integrating various healthcare data into a cohesive system supported by AI. The Machine Learning Engineer will build and manage ...

ManTech International

Machine Learning Engineer

$128.80K - $214.50K/yr

General information Requisition # R67616 Locations USA-Remote Work Posting Date 05/19/2026 Security ... MANTECH's benefits offerings include, dependent upon position, Health Insurance, Life Insurance ...

HealthEdge

Machine Learning Engineer

You'll partner directly with stakeholders across Engineering, Product, and healthcare professionals ... Work across multiple time zones in a hybrid or remote work environment. * Long periods of time ...

HealthEdge

Machine Learning Engineer

You'll partner directly with stakeholders across Engineering, Product, and healthcare professionals ... Work across multiple time zones in a hybrid or remote work environment. * Long periods of time ...

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Remote Healthcare Machine Learning information

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$25.5K

$42.6K

$88K

How much do remote healthcare machine learning jobs pay per year?

As of Jun 1, 2026, the average yearly pay for remote healthcare machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Remote Healthcare Machine Learning Specialist, and why are they important?

To thrive as a Remote Healthcare Machine Learning Specialist, you need a strong background in data science, statistics, machine learning algorithms, and healthcare domain knowledge, typically supported by a relevant degree in computer science, engineering, or biomedical informatics. Proficiency with programming languages (such as Python or R), machine learning frameworks (like TensorFlow or PyTorch), and experience with electronic health record (EHR) systems or health data standards is essential. Strong problem-solving skills, attention to detail, and the ability to communicate complex technical concepts to non-technical stakeholders make someone stand out in this role. These skills are crucial for developing effective, compliant, and impactful healthcare solutions that improve patient outcomes and enable remote care delivery.

How does a Remote Healthcare Machine Learning professional typically collaborate with clinical teams to implement AI solutions?

Remote Healthcare Machine Learning professionals often work closely with clinicians, data engineers, and IT staff to ensure that AI models address real clinical needs and comply with healthcare regulations. Collaboration usually involves regular virtual meetings, shared project management tools, and iterative feedback cycles where clinicians provide insights on data relevance and model outputs. Effective communication is crucial to bridge the gap between technical and medical expertise, ensuring solutions are both accurate and practical for everyday clinical use.

What is a Remote Healthcare Machine Learning professional?

A Remote Healthcare Machine Learning professional is someone who applies machine learning techniques and data analysis to healthcare-related problems while working remotely. They develop algorithms and models to analyze medical data, predict patient outcomes, and improve healthcare delivery. These professionals may work on projects like disease prediction, medical imaging analysis, or personalized treatment recommendations, often as part of a distributed team. Their work helps healthcare organizations leverage data to make informed decisions and improve patient care, all while working from a location outside of a traditional office or hospital setting.

What is the difference between Remote Healthcare Machine Learning vs Remote Healthcare Data Analyst?

AspectRemote Healthcare Machine LearningRemote Healthcare Data Analyst
Required CredentialsDegree in Computer Science, Data Science, or related field; knowledge of ML algorithmsDegree in Statistics, Data Analysis, or related field; proficiency in data visualization
Work EnvironmentCollaborates with data scientists and engineers; focuses on developing modelsAnalyzes healthcare data; reports insights to stakeholders
Industry UsageDevelops predictive models for patient outcomes, diagnosticsInterprets healthcare data to inform decisions and improve processes

Remote Healthcare Machine Learning specialists focus on creating algorithms and models to predict health trends, while Remote Healthcare Data Analysts interpret healthcare data to support decision-making. Both roles require strong analytical skills but differ in technical focus and responsibilities.

More about Remote Healthcare Machine Learning jobs
What cities are hiring for Remote Healthcare Machine Learning jobs? Cities with the most Remote Healthcare Machine Learning job openings:
What are the most commonly searched types of Healthcare Machine Learning jobs? The most popular types of Healthcare Machine Learning jobs are:
What states have the most Remote Healthcare Machine Learning jobs? States with the most job openings for Remote Healthcare Machine Learning jobs include:
What job categories do people searching Remote Healthcare Machine Learning jobs look for? The top searched job categories for Remote Healthcare Machine Learning jobs are:
Remote Healthcare Machine Learning jobs near you
Infographic showing various Remote Healthcare Machine Learning job openings in the United States as of May 2026, with employment types broken down into 86% Full Time, and 14% Contract. Highlights an 100% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.
Machine Learning Engineer (Remote)

Machine Learning Engineer (Remote)

Astrix Inc

South San Francisco, CA • On-site, Remote

$55 - $73/hr

Full-time

Posted 2 days ago

Job description

Our client is a leader in healthcare innovation, seamlessly integrating pharmaceutical development, diagnostic solutions, and advanced technology and data capabilities.
Title: Machine Learning Engineer (Contract)
Pay rate: $55-73/hr+ (Depends on experience)
Location: Remote in the US or Canada, or onsite in SSF. Must be available during PST hours.
Duration: Through Dec. 2026 (Likely to get extended)
Overview:
Seeking a Machine Learning Bioinformatics Engineer to develop and deploy advanced ML solutions supporting pharmaceutical R&D. This role focuses on analyzing large-scale, multimodal clinicogenomic datasets (genomic, transcriptomic, clinical, and real-world data) to drive insights into disease biology, patient stratification, and treatment response. Ideal candidates are strong in both machine learning and bioinformatics, with a passion for translating complex data into impactful discoveries.
Key Responsibilities:
  • Build and deploy scalable, production-ready machine learning models
  • Process and analyze genomic and transcriptomic data using bioinformatics pipelines
  • Prepare high-quality, normalized biological datasets for downstream analysis
  • Train large-scale models using frameworks like PyTorch Lightning and Hugging Face
  • Develop cloud-based ML solutions (AWS/GCP) with a focus on scalability and reproducibility
  • Collaborate with cross-functional teams to uncover biomarkers and therapeutic targets
  • Provide technical input and guidance on ML system design and implementation

Qualifications:
  • PhD with 0-2 years of relevant work experience, or MS with 3-5 years of relevant work experience, or BS with 4-7 years of relevant work experience.
  • Proficient programming skills: Strong Python programming skills with extensive experience in ML and data libraries (e.g., NumPy, pandas, PyTorch).
  • Deep ML expertise: Excellent knowledge of modern machine learning methods and development best practices, including training strategies, model validation, performance visualization, and experimental design.
  • Deep bioinformatic expertise: Proficient knowledge of bioinformatic processing pipelines for genomic and transcriptomic variables.
  • Strong knowledge of computational oncology, cancer genomics and analysis of clinicogenomics datasets.
  • Must be authorized to work in the United States

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