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Overnight Remote Machine Learning Jobs in Virginia

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site +1

Vienna, VA / Chantilly, VA (Hybrid / Flexible Remote options available) Responsibilities * Prototype to Production: Support the full machine learning lifecycle, taking computer vision models from ...

New

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... This internship is primarily a remote opportunity. However, if you are located near one of our ...

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 ...

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Overnight Remote Machine Learning information

What is an overnight remote machine learning job?

Overnight Remote Machine Learning jobs are positions where professionals work on machine learning tasks outside of traditional office hours, typically during the night, and do so from a remote location. These roles may involve building models, analyzing data, or maintaining machine learning systems while collaborating with teams in different time zones or providing 24/7 support. Overnight shifts can be critical for companies with global operations or those that require continuous system monitoring. Working remotely allows for flexibility and access to a wider talent pool. These positions often require strong programming and analytical skills, as well as the ability to work independently with minimal supervision.

What are the key skills and qualifications needed to thrive as an overnight remote machine learning engineer?

To thrive as an Overnight Remote Machine Learning Engineer, you need strong programming skills (especially in Python), a solid background in statistics and algorithms, and a relevant degree in computer science or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch, experience with cloud platforms like AWS or GCP, and knowledge of version control systems are typically required. Excellent problem-solving abilities, self-motivation, and clear written communication are crucial soft skills for remote and overnight work schedules. These competencies ensure that you can efficiently develop, deploy, and monitor machine learning models independently while collaborating across time zones.

What are some common challenges faced by overnight remote machine learning professionals, and how can they be addressed?

Overnight remote machine learning professionals often encounter challenges like coordinating with daytime teams across different time zones, maintaining effective communication, and managing alertness during non-traditional hours. To address these, it's helpful to establish clear communication protocols, use collaboration tools for asynchronous updates, and set a structured sleep and work routine to ensure productivity. Additionally, leveraging automated monitoring and robust documentation helps in managing handoffs and reducing errors during shift changes.

What is the difference between Overnight Remote Machine Learning vs Data Scientist?

AspectOvernight Remote Machine LearningData Scientist
CredentialsBachelor's or higher in CS, ML, or related fields; certifications like AWS, TensorFlowBachelor's or higher in CS, Statistics, or related fields; advanced degrees common
Work EnvironmentRemote, overnight shifts, focused on model deployment and data pipelinesOffice or remote, standard hours, focused on data analysis and model development
Industry UsageTech, finance, healthcare companies with 24/7 operationsResearch, tech, consulting firms, often with flexible hours

Overnight Remote Machine Learning roles typically focus on deploying models and maintaining data pipelines during overnight hours, often requiring specific certifications and remote work setups. Data Scientists usually work during regular hours, concentrating on data analysis, model development, and research. Both roles are vital in tech-driven industries but differ mainly in work hours, environment, and focus areas.

What are the most commonly searched types of Remote Machine Learning jobs in Virginia?

The most popular types of Remote Machine Learning jobs in Virginia are:

What are popular job titles related to Overnight Remote Machine Learning jobs in Virginia?

For Overnight Remote Machine Learning jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Overnight Remote Machine Learning jobs in Virginia look for?

The top searched job categories for Overnight Remote Machine Learning jobs in Virginia are:

Overnight Remote Machine Learning jobs near you
Infographic showing various Overnight Remote Machine Learning job openings in Virginia as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer

NT Concepts

Chantilly, VA • On-site, Remote

Full-time

Posted 3 days ago

New

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.

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