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Remote Python Machine Learning Jobs in Washington, DC

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site +1

Vienna, VA / Chantilly, VA (Hybrid / Flexible Remote options available) Responsibilities ... Strong programming skills in Python and hands-on experience with deep learning frameworks ...

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 ... Important Skills * Several years of experience with Python and machine learning frameworks

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

As of Sep 9, 2026, the average hourly pay for remote python machine learning in Washington, DC is $66.39, according to ZipRecruiter salary data. Most workers in this role earn between $54.71 and $75.43 per hour, depending on experience, location, and employer.

What is a remote Python machine learning?

A Remote Python Machine Learning job involves developing, deploying, and optimizing machine learning models using Python while working from a remote location. Responsibilities typically include data preprocessing, model training, evaluation, and integration into production systems. Professionals in this role often use frameworks like TensorFlow, PyTorch, or Scikit-learn and work with cloud platforms or on-premise infrastructure. This job requires strong programming skills, an understanding of machine learning algorithms, and experience handling large datasets. Remote positions offer flexibility but require self-discipline and effective communication with distributed teams.

What does a remote Python machine learning professional do?

A typical day in this role involves designing, developing, and testing machine learning models using Python, as well as cleaning and preprocessing large datasets. You may also spend time researching new algorithms, tuning model performance, and collaborating with data engineers, product managers, and other remote team members to integrate solutions into production. Regular code reviews, virtual meetings, and documentation are part of the workflow to ensure consistent project progress and maintain code quality. Balancing independent deep work with remote teamwork is key to succeeding in this environment.

What are the key skills and qualifications needed to thrive in remote Python machine learning?

To thrive as a Remote Python Machine Learning professional, you need a strong background in Python programming, machine learning algorithms, and data analysis, typically supported by a degree in computer science or a related field. Familiarity with libraries such as TensorFlow, PyTorch, scikit-learn, and experience using cloud platforms like AWS or Azure, as well as relevant certifications, are highly valuable. Excellent problem-solving skills, self-motivation, and clear communication are essential for remote collaboration and delivering impactful results. These capabilities enable you to tackle complex projects efficiently, drive innovation, and function effectively in distributed teams.

What are the most commonly searched types of Python Machine Learning jobs in Washington, DC?

The most popular types of Python Machine Learning jobs in Washington, DC are:

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

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

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

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

Remote Python Machine Learning 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 4 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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