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Python Machine Learning 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 ...

AI Squared

Machine Learning Engineer

Washington, DC · On-site

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled ... Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow. * Deep ...

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How much do python machine learning jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for python machine learning in Washington 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 Python machine learning?

A Python Machine Learning job involves developing, training, and deploying machine learning models using Python. Professionals in this role work with libraries like TensorFlow, scikit-learn, and PyTorch to analyze data, build predictive models, and optimize algorithms. Responsibilities often include data preprocessing, feature engineering, model evaluation, and deploying models to production environments. These roles are commonly found in industries like finance, healthcare, and e-commerce, where data-driven decision-making is crucial.

What does a typical workday look like for a Python machine learning professional?

A typical workday for a Python Machine Learning professional often involves tasks like cleaning and pre-processing data, developing and training machine learning models, and evaluating their performance using statistical metrics. You'll collaborate with data engineers, data scientists, and product managers to understand business requirements and integrate models into production environments. Regularly, you'll participate in code reviews, team meetings, and troubleshooting sessions to optimize model performance and address any issues. This dynamic role requires both independent project work and frequent cross-functional collaboration to ensure that solutions meet real-world needs.

What are the key skills and qualifications needed to thrive in the Python machine learning position, and why are they important?

To thrive as a Python Machine Learning professional, you need a strong background in statistics, programming (especially Python), data analysis, and machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Proficiency in libraries and frameworks like scikit-learn, TensorFlow, PyTorch, and familiarity with data visualization and version control tools are highly valued, as are relevant certifications such as TensorFlow Developer or AWS Machine Learning. Strong problem-solving ability, effective communication, and teamwork skills are important for collaboration and translating technical findings to non-technical stakeholders. These competencies enable you to design, develop, and deploy robust machine learning models that drive business solutions and innovation.

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

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

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

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

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

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

Python Machine Learning jobs near you
Infographic showing various Python Machine Learning job openings in Washington as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 18% Part Time, 2% Temporary, and 5% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $138,100 per year, or $66.4 per hour.

AI/ML Engineer

Mclean, VA • On-site

Virtual Networx
IT Services • 51 - 200 employees
Learn more about Virtual Networxexternal

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Re-posted yesterday

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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