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Junior Ai Machine Learning Python Jobs in Washington

Ai Squared

Machine Learning Engineer

Washington, DC

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

AI Squared

Machine Learning Engineer

Washington, DC · On-site

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

AI Squared

Machine Learning Engineer

Washington, DC

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

AI Squared

Machine Learning Engineer

Washington, DC

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

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Junior Ai Machine Learning Python information

What are the key skills and qualifications needed to thrive as a Junior AI Machine Learning Python Engineer, and why are they important?

To thrive as a Junior AI Machine Learning Python Engineer, you need a solid understanding of Python programming, statistics, and foundational machine learning concepts, often supported by a degree in computer science or a related field. Familiarity with tools and frameworks like TensorFlow, Scikit-learn, Jupyter Notebooks, and version control systems such as Git is typically required. Strong problem-solving abilities, attention to detail, and effective teamwork skills help individuals excel in collaborative and fast-evolving technical environments. These competencies are crucial for developing robust AI solutions, learning from senior colleagues, and adapting to the rapidly changing landscape of machine learning.

What does a Junior AI Machine Learning Python engineer do?

A Junior AI Machine Learning Python engineer assists in developing, testing, and maintaining machine learning models using Python. They typically work with data preparation, preprocessing, and applying basic algorithms to solve real-world problems. Under the guidance of senior engineers, they help implement solutions, evaluate model performance, and may contribute to the deployment of models into production environments. Their role often includes learning best practices in coding, software development, and collaborating with data scientists and engineers.

What are some typical projects or tasks a Junior AI/Machine Learning Python developer might work on in their first year?

As a Junior AI/Machine Learning Python developer, you can expect to work on tasks such as cleaning and preparing datasets, developing and testing simple machine learning models, and assisting in the implementation of algorithms under the supervision of senior team members. You may also help automate data pipelines, write scripts for data extraction, and contribute to model evaluation and reporting. Collaboration with data scientists, software engineers, and product managers is common, providing valuable learning opportunities and exposure to the full machine learning workflow.

What is the difference between Junior Ai Machine Learning Python vs Data Analyst?

AspectJunior Ai Machine Learning PythonData Analyst
Required SkillsPython, Machine Learning, AI concepts, data preprocessingExcel, SQL, data visualization, basic statistical analysis
CertificationsPython certifications, AI/ML coursesData analysis or visualization certifications
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, marketing departments
Industry UsageDeveloping AI models, machine learning pipelinesInterpreting data, generating reports, supporting decision-making

Junior Ai Machine Learning Python roles focus on developing AI models using Python and machine learning techniques, often in tech-driven environments. Data Analysts primarily interpret data, create visualizations, and support business decisions. While both roles require analytical skills, AI/ML roles demand programming and AI-specific knowledge, whereas Data Analysts focus on data interpretation and reporting.

What are popular job titles related to Junior Ai Machine Learning Python jobs in Washington? For Junior Ai Machine Learning Python jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Junior Ai Machine Learning Python jobs in Washington look for? The top searched job categories for Junior Ai Machine Learning Python jobs in Washington are:
What cities in Washington are hiring for Junior Ai Machine Learning Python jobs? Cities in Washington with the most Junior Ai Machine Learning Python job openings:
Junior Ai Machine Learning Python jobs near you
Infographic showing various Junior Ai Machine Learning Python job openings in Washington as of June 2026, with employment types broken down into 84% Full Time, 12% Part Time, 1% Contract, and 3% Nights. Highlights an 62% Physical, 4% Hybrid, and 34% Remote job distribution.
Machine Learning Engineer

Machine Learning Engineer

Ai Squared

Washington, DC

Other

Posted 28 days ago

Job description

Machine Learning Engineer

Washington, DC (Hybrid)

About the Role:

We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying, maintaining, and monitoring the AI/ML systems that power our platform. You will work closely with data scientists, data engineers, and product teams to ensure scalable, reliable, and production-grade AI solutions. You'll play a critical role in operationalizing large language models (LLMs) and other ML systems, ensuring they run efficiently, securely, and with robust monitoring in place.

Key Responsibilities:

  • Design, implement, and maintain ML deployment pipelines for scalable production systems.
  • Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability.
  • Build robust model monitoring, logging, and alerting systems to track performance and detect drift.
  • Partner with data scientists to transition models from research/prototype into production-ready deployments.
  • Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment.
  • Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems.
  • Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems.
  • Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements.

Qualifications:

  • 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
  • Proven experience deploying and maintaining machine learning models in production at scale.
  • Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
  • Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
  • Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems.
  • Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling.
  • Strong understanding of MLOps best practices, monitoring, and automation.
  • Excellent problem-solving skills, with an emphasis on building reliable, scalable systems.
  • Strong communication and collaboration skills across technical and non-technical teams.

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