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Remote Biomedical Machine Learning Jobs in Ashburn, VA

10a Labs

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

About the Role We are seeking a Machine Learning Engineer to design, build, and evaluate advanced ... Fully remote, U.S.-based * Health Benefits : Comprehensive health, dental, and vision coverage

Spear AI

Machine Learning Engineer

Washington, DC · On-site +1

We're seeking a skilled Machine Learning Engineer to build and deploy production ML systems for the ... Onsite / Remote / Flexible work arrangements or hybrid options (position dependent) * Relocation ...

Halvik

Machine Learning Engineer - Remote

Vienna, VA · On-site +1

$140K - $150K/yr

Halvik is a highly successful WOB business with more than 50 prime contracts and 500+ professionals delivering Digital Services, Advanced Analytics, Artificial Intelligence/Machine Learning ...

Niyam IT, Inc.

AI Machine Learning Engineer

Ashburn, VA · On-site +1

Remote Work: Niyam understands the value of flexibility. We offer remote work. * Career Growth ... The ideal candidate brings a strong foundation in machine learning, data engineering, and MLOps ...

Comcast

Engineer 3, Machine Learning-5125

Washington, DC · On-site +1

$126K - $165K/yr

... for the remote option.) Job Summary DUTIES: Contribute to a team responsible for building ... train Machine Learning models, including Deep Learning models, using TensorFlow, PyTorch ...

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Remote Biomedical Machine Learning information

See Ashburn, VA salary details

$15

$29

$39

How much do remote biomedical machine learning jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for remote biomedical machine learning in Ashburn, VA is $29.17, according to ZipRecruiter salary data. Most workers in this role earn between $24.81 and $32.93 per hour, depending on experience, location, and employer.

What is a remote biomedical machine learning job?

Remote biomedical machine learning jobs involve applying machine learning and artificial intelligence techniques to biomedical data, such as medical images, genetic information, or clinical records, while working from a remote location. Professionals in these roles develop algorithms to assist in disease diagnosis, drug discovery, or patient outcome prediction. These jobs typically require strong programming skills, experience with data science tools, and a background in biomedical sciences or related fields. Remote positions offer flexibility and the ability to collaborate with interdisciplinary teams from anywhere in the world.

What are some unique challenges faced when working remotely as a biomedical machine learning professional, and how can they be addressed?

Remote Biomedical Machine Learning professionals often face challenges related to accessing large and sensitive datasets, ensuring compliance with data privacy regulations, and maintaining effective communication with interdisciplinary teams such as clinicians and researchers. To address these, it's important to become familiar with secure data transfer protocols, collaborate closely with IT and compliance officers, and utilize robust project management and communication tools. Regular virtual meetings and clear documentation can help bridge gaps and ensure alignment on project goals.

What are the key skills and qualifications needed to thrive as a remote biomedical machine learning specialist, and why are they important?

Thriving in Remote Biomedical Machine Learning requires expertise in machine learning, data analysis, and a strong background in biomedical sciences, often supported by an advanced degree in a related field. Proficiency with programming languages such as Python or R, experience with frameworks like TensorFlow or PyTorch, and familiarity with medical data systems are typically necessary. Excellent problem-solving skills, communication abilities, and self-motivation are standout soft skills for remote collaboration and research. These competencies are vital to effectively develop innovative biomedical solutions, ensure data integrity, and drive impactful research in a distributed work environment.

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

AspectRemote Biomedical Machine LearningRemote Biomedical Data Analyst
Required CredentialsMaster's or PhD in Bioinformatics, Data Science, or related fields; experience with ML frameworksBachelor's or Master's in Biology, Data Analysis, or related; proficiency in data visualization and statistical tools
Work EnvironmentCollaborative remote teams, research labs, tech companiesRemote healthcare organizations, research institutions, biotech firms
Employer & Industry UsageTech companies, biotech startups, research institutionsHospitals, healthcare providers, pharmaceutical companies

Remote Biomedical Machine Learning specialists focus on developing algorithms and models to analyze biomedical data, often requiring advanced degrees and programming skills. In contrast, Remote Biomedical Data Analysts interpret and visualize biomedical datasets, typically with a focus on statistical analysis. Both roles are vital in healthcare and biotech industries but differ in technical depth and responsibilities.

What are popular job titles related to Remote Biomedical Machine Learning jobs in Ashburn, VA?

For Remote Biomedical Machine Learning jobs in Ashburn, VA, the most frequently searched job titles are:

What job categories do people searching Remote Biomedical Machine Learning jobs in Ashburn, VA look for?

The top searched job categories for Remote Biomedical Machine Learning jobs in Ashburn, VA are:

What cities near Ashburn, VA are hiring for Remote Biomedical Machine Learning jobs?

Cities near Ashburn, VA with the most Remote Biomedical Machine Learning job openings:

Remote Biomedical Machine Learning jobs near you

Machine Learning Engineer

10a Labs

Washington, DC • On-site, Remote

$130K - $200K/yr

Full-time

Medical, Dental, Vision, PTO

Re-posted 18 days ago

Job description

About the Role

We are seeking a Machine Learning Engineer to design, build, and evaluate advanced machine learning systems across AI safety and model evaluation applications.

This role combines strong ML engineering with an experimental mindset. You will work on problems involving reinforcement learning, model evaluations, language models, multimodal systems, and classifiers, taking ambiguous technical questions and turning them into rigorous experiments and scalable systems.

You will collaborate closely with engineers, analysts, red teamers, and subject-matter experts supporting leading AI organizations.

What You'll Do
  • Design and run ML experiments to evaluate the capabilities, behavior, robustness, and limitations of advanced AI systems.
  • Develop and evaluate models across reinforcement learning, NLP/LLMs, computer vision, and multimodal ML.
  • Build evaluation pipelines, benchmarks, datasets, and metrics for frontier AI systems.
  • Train, fine-tune, and evaluate models for safety, security, and other high-impact applications.
  • Develop reliable tooling and infrastructure to run ML experiments and evaluations at scale.
  • Analyze results, identify model failure modes, and translate findings into new experiments and technical approaches.
What We're Looking For
  • 3-5+ years of experience in machine learning, research engineering, or a related technical field.
  • Strong Python skills and experience with ML frameworks such as PyTorch or JAX.
  • Hands-on experience training, fine-tuning, or evaluating modern ML models.
  • Strong understanding of experimental design, model evaluation, and quantitative analysis.
  • Familiarity with agentic AI fundamentals, including common harnesses, Model Context Protocol, agent benchmarks, and security risks to AI agents.
  • Experience in one or more of the following: reinforcement learning, NLP/LLMs, computer vision, or multimodal ML.
  • Strong software engineering fundamentals and the ability to work independently on ambiguous technical problems.
Nice to Have
  • Experience with RLHF/RLAIF, reward modeling, policy optimization, or other model post-training techniques.
  • Experience evaluating frontier language or multimodal models.
  • Experience with adversarial evaluations, robustness testing, or AI safety.
  • Experience with distributed training, cloud ML infrastructure, or large-scale ML systems.

We don't expect candidates to have experience across every area above. We value deep ML expertise, strong experimental instincts, and the ability to quickly learn new techniques.

Compensation & Benefits
  • Salary Range: $130K-$200K, depending on experience and location
  • Bonus: Performance-based annual bonus
  • Professional Development: Support for conferences, continuing education, or leadership training
  • Work Environment: Fully remote, U.S.-based
  • Health Benefits: Comprehensive health, dental, and vision coverage
  • Time Off: Generous PTO and paid holiday schedule

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