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Remote Machine Learning Biology Jobs in Washington, DC

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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All JobsMachine Learning Biology JobsRemote Machine Learning Biology JobsRemote Machine Learning Biology Jobs in Washington, DC

Remote Machine Learning Biology information

See Washington, DC salary details

$41.9K

$101.3K

$156.3K

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

As of Aug 19, 2026, the average yearly pay for remote machine learning biology in Washington, DC is $101,258.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,400.00 and $137,000.00 per year, depending on experience, location, and employer.

What is a remote machine learning biologist?

A Remote Machine Learning Biologist is a professional who applies machine learning techniques to biological data and problems while working remotely, often from home or a location outside a traditional laboratory or office. They use computational tools and algorithms to analyze complex biological datasets, such as genomics, proteomics, or drug discovery data, to derive insights or make predictions. Their work may involve developing predictive models, automating data analysis, and collaborating with life scientists and engineers. Remote roles in this field require strong skills in both biology and computer science, as well as the ability to work independently and communicate effectively with remote teams.

What are the key skills and qualifications needed to thrive as a remote machine learning biology professional?

To thrive as a Remote Machine Learning Biology professional, you need a strong foundation in computational biology, machine learning algorithms, programming (such as Python or R), and a relevant degree in bioinformatics, computer science, or biology. Familiarity with bioinformatics tools, data analysis platforms, cloud computing resources, and frameworks like TensorFlow or PyTorch is typically required. Excellent problem-solving, collaboration, and communication skills are essential for effectively interpreting results and working with interdisciplinary teams in a remote environment. These skills and qualities are crucial for advancing biological research through data-driven insights and ensuring effective teamwork and project delivery in a virtual setting.

How do remote machine learning biology professionals typically collaborate with experimental biologists and other team members?

Remote machine learning biology professionals often work closely with experimental biologists, bioinformaticians, and data engineers through virtual meetings, shared project management tools, and collaborative coding platforms. Regular communication is vital to ensure alignment on research objectives, data requirements, and interpretation of results. Team members typically share data, code, and experimental findings using cloud-based repositories, while frequent check-ins help address challenges and maintain project momentum. This collaborative approach allows remote professionals to contribute effectively to interdisciplinary research, despite physical distance.

What is the difference between Remote Machine Learning Biology vs Remote Bioinformatics Specialist?

AspectRemote Machine Learning BiologyRemote Bioinformatics Specialist
Required CredentialsMaster's or PhD in Biology, Data Science, or related fields; experience in machine learningBachelor's or Master's in Bioinformatics, Biology, or Computer Science; programming skills
Work EnvironmentResearch labs, biotech companies, or academic institutions with remote optionsResearch institutions, healthcare, or biotech firms with remote roles
Industry UsageUsed in biotech, pharmaceuticals, and research to analyze biological data with MLApplied in genomics, proteomics, and clinical data analysis

Remote Machine Learning Biology focuses on applying machine learning techniques to biological data, often requiring advanced degrees and programming skills. Remote Bioinformatics Specialists analyze biological datasets using bioinformatics tools. Both roles are vital in biotech and research industries, but they differ in technical focus and required expertise.

What are popular job titles related to Remote Machine Learning Biology jobs in Washington, DC?

For Remote Machine Learning Biology jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Remote Machine Learning Biology jobs in Washington, DC look for?

The top searched job categories for Remote Machine Learning Biology jobs in Washington, DC are:

Remote Machine Learning Biology jobs near you

Machine Learning Engineer

10a Labs

Washington, DC • On-site, Remote

$130K - $200K/yr

Full-time

Medical, Dental, Vision, PTO

Re-posted 16 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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