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Scikit Learn Jobs in Washington (NOW HIRING)

Virtual Networx

AI/ML Engineer

Mclean, VA · On-site

Python, Machine Learning, Deep Learning, Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, SQL, NLP, Computer Vision, Generative AI, LLM, Prompt Engineering, RAG, Vector Databases, REST APIs ...

VTG

Data Scientist

Chantilly, VA · On-site

Experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch) * Strong foundation in statistics, probability, and data analysis techniques * Experience with SQL and working ...

VTG

Data Scientist

Herndon, VA · On-site

Experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch) * Strong foundation in statistics, probability, and data analysis techniques * Experience with SQL and working ...

VTG

Data Scientist

Mclean, VA · On-site

Experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch) * Strong foundation in statistics, probability, and data analysis techniques * Experience with SQL and working ...

AITHERAS, LLC

Junior Data Scientist

Arlington, VA · On-site

Hands-on Python experience using pandas, NumPy, scikit-learn, matplotlib, spaCy, Keras, or similar libraries. * R experience using tidyverse, tidymodels, ggplot2, Shiny, or equivalent packages. * SQL ...

AITHERAS, LLC

Junior Data Scientist

Arlington, VA · On-site

$100K - $120K/yr

Hands-on Python experience using pandas, NumPy, scikit-learn, matplotlib, spaCy, Keras, or similar libraries. * R experience using tidyverse, tidymodels, ggplot2, Shiny, or equivalent packages. * SQL ...

Staffingine LLC

AI Platform Developer

Herndon, VA · On-site

Proficiency is required in tools like TensorFlow, PyTorch, Keras, and scikit-learn. * Data Science and Analysis: Skills in data acquisition, cleaning, preprocessing, and feature engineering are ...

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Scikit Learn information

What is a scikit learn job?

A Scikit Learn job typically involves using the Scikit Learn library in Python for machine learning tasks such as data preprocessing, model training, and evaluation. Professionals in these roles often work as data scientists, machine learning engineers, or AI researchers to develop predictive models and enhance data-driven decision-making. Responsibilities may include implementing supervised and unsupervised learning algorithms, tuning hyperparameters, and integrating models into production systems. Strong proficiency in Python, statistical analysis, and experience with libraries like NumPy and pandas are often required.

What are the typical day-to-day responsibilities in a scikit learn job?

In a Scikit-learn-focused role, your day-to-day responsibilities often include developing and testing machine learning models, performing data preprocessing, feature engineering, and evaluating model performance using Scikit-learn's extensive toolkit. You may collaborate closely with data engineers, software developers, and business analysts to understand project requirements and integrate models into production systems. Regular tasks can also involve tuning hyperparameters, interpreting model outputs, and documenting your work for future reference or team knowledge sharing. This blend of technical and collaborative tasks helps ensure your models are both effective and aligned with business goals.

What are the key skills and qualifications needed to thrive in a scikit learn position?

To excel in roles focused on Scikit-learn, you need a solid background in machine learning, programming in Python, and statistical analysis, often backed by a degree in computer science, data science, or a related field. Familiarity with Scikit-learn's API, version control systems like Git, and practical experience using Jupyter notebooks or similar development environments are typically required. Strong problem-solving skills, attention to detail, and the ability to communicate complex technical concepts to stakeholders are vital soft skills. These abilities are crucial for developing effective machine learning solutions and collaborating efficiently within multidisciplinary data teams.

What are popular job titles related to Scikit Learn jobs in Washington?

For Scikit Learn jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Scikit Learn jobs in Washington look for?

The top searched job categories for Scikit Learn jobs in Washington are:

Scikit Learn jobs near you
Infographic showing various Scikit Learn job openings in Washington as of August 2026, with employment types broken down into 79% Full Time, 18% Part Time, and 3% Contract. Highlights an 79% Physical, 1% Hybrid, and 20% Remote job distribution.

AI/ML Engineer

Virtual Networx

Mclean, VA • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Role: AI/ML Engineer
Experience: 10+ Years
Duration: 12 months
Location: MC Lean , VA

Skills: Python, Machine Learning, Deep Learning, Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, SQL, NLP, Computer Vision, Generative AI, LLM, Prompt Engineering, RAG, Vector Databases, REST APIs

Responsibilities:

  • Design, develop, and deploy Machine Learning and AI solutions for business applications.
  • Build and optimize ML models for classification, regression, forecasting, recommendation, and NLP use cases.
  • Develop data preprocessing, feature engineering, model training, and evaluation pipelines.
  • Work with Python, Pandas, NumPy, Scikit-learn, TensorFlow, and/or PyTorch.
  • Develop and integrate Generative AI and LLM-based solutions where applicable.
  • Work with OpenAI/LLM APIs, prompt engineering, embeddings, vector databases, and RAG architectures.
  • Build scalable ML pipelines using MLflow, Kubeflow, Databricks, AWS, Azure, or Google Cloud Platform.
  • Deploy models through REST APIs, Docker, Kubernetes, and cloud platforms.
  • Monitor model performance, data quality, drift, and production issues.
  • Collaborate with Data Engineers, Software Engineers, Data Scientists, Product Owners, and business stakeholders.
  • Perform model tuning, experimentation, validation, and performance optimization.
  • Implement MLOps practices for CI/CD, model versioning, experiment tracking, and automated deployment.
  • Ensure AI solutions meet requirements for security, scalability, reliability, and responsible AI.
  • Document models, architectures, workflows, and technical processes.

Required Skills

  • Python
  • Machine Learning
  • Deep Learning
  • Scikit-learn
  • TensorFlow / PyTorch
  • Pandas / NumPy
  • SQL
  • NLP / Computer Vision as applicable
  • Generative AI / LLM
  • Prompt Engineering
  • RAG
  • Vector Databases
  • REST APIs
  • Docker / Kubernetes
  • Cloud: AWS / Azure / Google Cloud Platform
  • Git
  • MLOps / MLflow

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