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Remote Full Stack Machine Learning Engineer Jobs in Virginia

Lead Machine Learning Engineer

Mclean, VA ยท On-site +1

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Lead Machine Learning Engineer

Mclean, VA ยท On-site +1

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Lead Machine Learning Engineer

Mclean, VA ยท On-site +1

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Lead Machine Learning Engineer

Mclean, VA ยท On-site +1

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Sr. Lead Machine Learning Engineer

Mclean, VA ยท On-site +1

$103K - $136K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Lead Machine Learning Engineer

Mclean, VA ยท On-site +1

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Lead Machine Learning Engineer

Mclean, VA ยท On-site +1

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Lead Machine Learning Engineer

Mclean, VA ยท On-site +1

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

Lead Machine Learning Engineer

Mclean, VA ยท On-site +1

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Eligibility varies based on full or part-time status, exempt or non-exempt status, and management ...

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Showing results 1-20

Remote Full Stack Machine Learning Engineer information

What is a Remote Full Stack Machine Learning Engineer?

A Remote Full Stack Machine Learning Engineer is a professional who designs, develops, and deploys machine learning solutions while working remotely. They handle both the front-end and back-end aspects of machine learning projects, including data preprocessing, model building, API development, and integration with user interfaces or cloud platforms. This role requires expertise in programming, machine learning frameworks, cloud services, and web technologies, allowing them to build end-to-end AI-driven applications from anywhere in the world.

What are some common challenges faced by remote Full Stack Machine Learning Engineers, and how can they be addressed?

Remote Full Stack Machine Learning Engineers often encounter challenges such as managing effective collaboration with cross-functional teams and ensuring smooth deployment of machine learning models into production environments. To address these, it's important to establish clear communication channels, regularly participate in virtual stand-ups, and use collaborative platforms such as GitHub and Slack. Additionally, staying organized with version control and thorough documentation helps maintain project transparency and ensures seamless handoffs between backend and frontend development. Proactively seeking feedback and scheduling regular check-ins with team members can further enhance productivity and integration within the team.

What is the difference between Remote Full Stack Machine Learning Engineer vs Remote Data Scientist?

AspectRemote Full Stack Machine Learning EngineerRemote Data Scientist
Primary FocusDeveloping end-to-end machine learning applications, including backend, frontend, and model deploymentAnalyzing data, creating models, and generating insights without necessarily building full applications
Skills RequiredProgramming (Python, JavaScript), ML frameworks, web development, deployment toolsStatistics, data analysis, visualization, Python/R, SQL
Work EnvironmentCollaborates with developers, data engineers, and product teams in tech-driven companiesWorks with data teams, analysts, and business units in various industries

While both roles involve working with data and machine learning, a Remote Full Stack Machine Learning Engineer builds complete applications with integrated ML models, whereas a Remote Data Scientist focuses on data analysis and model creation without necessarily developing full applications.

What are the key skills and qualifications needed to thrive as a Remote Full Stack Machine Learning Engineer, and why are they important?

To thrive as a Remote Full Stack Machine Learning Engineer, you need proficiency in programming languages (such as Python or JavaScript), a solid understanding of machine learning algorithms, experience with web development frameworks, and typically a degree in computer science or a related field. Familiarity with tools like TensorFlow, PyTorch, Docker, cloud computing platforms (AWS, GCP), and version control systems (Git) is essential. Strong problem-solving skills, self-motivation, and clear communication are crucial soft skills, especially in remote and cross-functional team environments. These combined skills ensure effective design, deployment, and integration of machine learning solutions in scalable web applications while maintaining productivity in a remote setting.
What are the most commonly searched types of Full Stack Machine Learning Engineer jobs in Virginia? The most popular types of Full Stack Machine Learning Engineer jobs in Virginia are:
What job categories do people searching Remote Full Stack Machine Learning Engineer jobs in Virginia look for? The top searched job categories for Remote Full Stack Machine Learning Engineer jobs in Virginia are:
What cities in Virginia are hiring for Remote Full Stack Machine Learning Engineer jobs? Cities in Virginia with the most Remote Full Stack Machine Learning Engineer job openings:

Software Engineer (Full Stack) - SME

GRVTY

Springfield, VA โ€ข Remote

Other

Re-posted 17 days ago


Job description

What Impact You'll Have:

Join a mission-focused team where your work directly supports critical national security objectives. We are seeking a Subject Matter Expert (SME) Full Stack Developer to lead the design, development, and delivery of scalable, mission-driven applications within an ML/Ops environment. This role combines deep technical expertise with advanced system-level thinking and close collaboration across engineering, data science, and customer stakeholder teams.

The Full Stack Developer will perform rapid application design, ETL, data analysis, and interpretation while developing rules and methodologies for data collection and analysis. You will architect, develop, and maintain a Python-based data warehouse processing system that serves as the backend for a user-facing application, while also leading development of modern GUI applications using REST APIs and contemporary web frameworks.

You will work closely with data scientists, computer vision engineers, ETL engineers, and intelligence analysts to integrate machine learning capabilities into production systems, enabling scalable model deployment, monitoring, and continuous improvement. This role emphasizes ownership, technical leadership, and delivery of production-ready solutions that operate reliably in dynamic, real-world environments.

What You'll Be Owning:

Lead and participate in the architectural design of complex features early in the development lifecycle.
Translate customer requirements and roadmap priorities into technical solutions, tasks, timelines, and resource plans.
Develop, integrate, and maintain full stack applications supporting ML/Ops pipelines and data-driven systems.
Design and implement scalable APIs and services to support machine learning model deployment and inference.
Develop and maintain data pipelines, ETL processes, and data storage solutions for large-scale datasets.
Collaborate with data scientists and ML engineers to operationalize models within production environments.
Optimize application and system performance for scalability, reliability, and efficiency, including edge and distributed environments when applicable.
Conduct peer reviews and establish coding standards to improve overall code quality and maintainability.
Guide development testing, exploratory testing, automated testing, and validation strategies.
Own code in production environments, respond to incidents, and lead root cause analysis and continuous improvement efforts.
Ensure security, compliance, and governance are maintained throughout the development lifecycle.
Perform technical planning, system integration, verification and validation, and risk assessments across system components.
Mentor and develop junior and mid-level engineers, fostering technical growth and high-performing teams.
Drive adoption of modern ML/Ops practices, tools, and automation frameworks across the team.

What You Must Have:

Active TS/SCI clearance with the ability to obtain a CI poly
Bachelor's degree in Computer Science, Engineering, or a related technical field.
14+ years of professional experience in full stack software development.
Expert-level proficiency in Python and object-oriented design patterns.
Extensive experience developing backend systems, APIs, and data processing pipelines.
Strong experience with modern web development frameworks, including React.js, Node.js, and/or Electron.
Deep understanding of data modeling techniques and experience working with large-scale and time series datasets.
Experience with relational and non-relational databases such as PostgreSQL, MongoDB, and BigQuery.
Experience building and maintaining RESTful APIs and microservices architectures.
Experience supporting machine learning workflows, including model integration, deployment, and monitoring.
Familiarity with ML/Ops tools and utilities such as MLflow, DVC, and/or Optuna.
Strong experience with Python libraries such as NumPy and Pandas.
Experience with Python web frameworks such as Flask, FastAPI, Pydantic, Gunicorn, and Uvicorn.
Experience with containerization and DevOps practices, including Docker and CI/CD pipelines.
Experience with web servers such as Apache and Nginx.
Experience working within Agile development environments and using associated tools.

What Would be Nice to Have:

Experience supporting government or defense-related programs.
Experience integrating computer vision or machine learning capabilities into operational systems.
Knowledge of real-time data processing, streaming architectures, or distributed systems.
Experience with cloud-based ML/Ops environments and infrastructure (AWS, Azure, or Google Cloud Platform).
Experience with parallelization and multiprocessing frameworks such as Dask.
Knowledge of geospatial data processing tools and libraries including GeoPandas, Shapely, Rasterio, QGIS, and ArcPy.
Experience with machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch.
Experience with remote procedure call technologies such as gRPC and JSON-RPC.

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