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Remote Machine Learning Researcher Jobs in Washington, DC

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site +1

Our machine learning teams bridge the gap between cutting-edge AI research and operational ... Vienna, VA / Chantilly, VA (Hybrid / Flexible Remote options available) Responsibilities

System One

Machine Learning/AI Engineer

Merrifield, VA · Remote

Hybrid in Vienna, VA or Remote Pay Rate: Open to Both W2 and established 1099 options (no c2c ... Research and evaluate emerging technologies. * Develop data science solutions based on tools and ...

Spear AI

Machine Learning Engineer

Washington, DC · On-site +1

... * We're remote - Work from wherever you want. We collaborate in real time on Slack or ... Contributions to open-source ML projects or research publications * Experience in defense ...

System One

Machine Learning/AI Engineer

Merrifield, VA · Remote

Hybrid in Vienna, VA or Remote Pay Rate: Open to Both W2 and established 1099 options (no c2c ... Research and evaluate emerging technologies. * Develop data science solutions based on tools and ...

System One

Machine Learning/AI Engineer

Merrifield, VA · Remote

Hybrid in Vienna, VA or Remote Pay Rate: Open to Both W2 and established 1099 options (no c2c ... Research and evaluate emerging technologies. * Develop data science solutions based on tools and ...

Halvik

Machine Learning Engineer - Remote

Mclean, 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 ...

Halvik

Machine Learning Engineer - Remote

Mclean, VA · Remote

$115K - $139K/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 ...

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Remote Machine Learning Researcher information

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$34K

$128.1K

$186.3K

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

As of Sep 14, 2026, the average yearly pay for remote machine learning researcher in Washington, DC is $128,099.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,900.00 and $174,400.00 per year, depending on experience, location, and employer.

What is a remote machine learning researcher?

Remote Machine Learning Researchers are professionals who study, design, and develop machine learning algorithms and models while working outside of a traditional office environment. They typically analyze data, conduct experiments, and collaborate with teams or organizations virtually to advance artificial intelligence technologies. Their work may involve tasks such as building predictive models, publishing research papers, or contributing to open-source machine learning projects. Being remote allows them flexibility in location and often the ability to work with international teams. Strong programming, mathematical, and communication skills are essential in this role.

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

To thrive as a Remote Machine Learning Researcher, you need a strong background in mathematics, statistics, programming (Python, R), and a relevant advanced degree such as a Master's or Ph.D. in computer science or a related field. Familiarity with machine learning frameworks (TensorFlow, PyTorch), cloud computing platforms, and version control systems (Git) is typically required, along with published research or contributions to academic conferences. Outstanding problem-solving ability, self-motivation, and excellent written communication are crucial soft skills for remote collaboration and knowledge sharing. These skills are essential for developing innovative models, contributing to cutting-edge research, and effectively collaborating in a distributed team environment.

What are the common challenges faced by remote machine learning researchers when collaborating with global teams?

Remote machine learning researchers often collaborate with team members across different time zones and cultural backgrounds, which can make synchronous meetings and real-time problem-solving challenging. Communication of complex ideas, such as model architectures or experimental results, may require extra effort through detailed documentation and regular virtual check-ins. However, most organizations use collaborative tools like version control systems, project management platforms, and video conferencing to bridge these gaps, ensuring that research progress stays on track and team members remain aligned.

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

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

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

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

Remote Machine Learning Researcher jobs near you

Machine Learning Engineer

Chantilly, VA • On-site, Remote

NT Concepts
IT Services • 51 - 200 employees
Learn more about NT Conceptsexternal

Full-time

Posted 10 days ago

Job description

 

We are seeking a Machine Learning Engineer with a passion for building mission-critical capabilities to join our talent network. Working at NT Concepts means that you are part of an innovative, agile company dedicated to solving the most critical challenges in National Security. If meaningful work, initiative, creativity, and continuous self-improvement are important to your career, explore What's Next with us.

Mission Focus: Our machine learning teams bridge the gap between cutting-edge AI research and operational government missions. We are looking for engineers who can take machine learning and Computer Vision (CV) solutions from early research and prototyping all the way into stable, scalable production environments.

 

In this role, you will help design, build, and deploy automated ML workflows that directly support national security analysts and operators. We embrace modern agile practices, a DataOps/DevSecOps/MLOps ethos to "automate-first," and modern cloud-native architectures.

Clearance: Active TS/SCI required (CI Polygraph preferred or must be eligible to obtain)

Location/Flexibility: Vienna, VA / Chantilly, VA (Hybrid / Flexible Remote options available)

Responsibilities 

  • Prototype to Production: Support the full machine learning lifecycle, taking computer vision models from experimentation and notebooks into containerized, high-throughput production microservices.
  • Mission Alignment: Work closely with mission partners, domain experts, and technical teams to understand real-world operational challenges and translate them into practical ML requirements.
  • MLOps & Pipeline Automation: Build, maintain, and optimize robust pipelines for data preparation, model training, validation, versioning, deployment, and monitoring using modern tools (such as MLflow, Kubeflow, and GitLab CI/CD).
  • Model Development & Tuning: Train, fine-tune, and evaluate deep learning algorithms for computer vision tasks (e.g., object detection, classification, segmentation, tracking).
  • System Integration: Collaborate with cross-functional software engineers and cloud architects to integrate ML models cleanly into larger enterprise systems and secure cloud infrastructures.
  • Optimization & Governance: Optimize inference performance, apply secure coding practices, and monitor models for drift and reliability once deployed. 

 Qualifications

  • Clearance: Active TS/SCI clearance.
  • Hands-On Experience: Demonstrated professional experience developing, testing, and deploying machine learning models into real-world or production environments.
  • Deep Learning & CV: Strong programming skills in Python and hands-on experience with deep learning frameworks (primarily PyTorch, OpenCV, TensorFlow, or NumPy).
  • ML Lifecycle & MLOps: Practical familiarity with containerization (Docker, Kubernetes) and ML lifecycle/pipeline platforms (e.g., MLflow, Kubeflow, AWS SageMaker).
  • Cloud & DevOps Foundations: Familiarity working in cloud environments (AWS, Azure, or GCP) and modern development practices (Git, CI/CD pipelines, Agile methodologies).
  • Customer & Mission Mindset: Ability to understand the end-user's mission objectives, iterate based on user feedback, and clearly communicate technical approaches. 

Preferred / Desired Skills:

  • Experience working within secure, air-gapped, or classified cloud environments (e.g., AWS GovCloud / C2S).
  • Experience with synthetic data generation techniques or multi-modal models.
  • Exposure to Large Language Models (LLMs) or generative AI workflows.
  • Familiarity with distributed model training and GPU resource management. 

Physical Requirements

  • Prolonged periods sitting at a desk and working on a computer.
  • Must be able to lift up to 10-15 pounds at times.

#JT

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