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Machine Learning Developer Intern Jobs in Rockville, MD

Machine Learning Engineer Schedule: Full-Time Shift: Day Job Travel: Yes - 10% of the time Minimum Clearance Required: TS.SCI Clearance Level Must Be Able to Obtain: None Potential for Remote Work:

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 ...

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 ...

Engineer, Machine Learning

Arlington, VA · On-site

$157K - $185K/yr

Engineer, Machine Learning Located: Arlington Summary The Machine Learning Engineer will design, develop, and maintain the productionization of machine learning, deep learning, generative AI, large ...

Machine Learning Engineer Schedule: Full-Time Shift: Day Job Travel: Yes - 10% of the time Minimum Clearance Required: TS.SCI Clearance Level Must Be Able to Obtain: None Potential for Remote Work:

Machine Learning Engineer Schedule: Full-Time Shift: Day Job Travel: Yes - 10% of the time Minimum Clearance Required: TS.SCI Clearance Level Must Be Able to Obtain: None Potential for Remote Work:

Showing results 21-40

Machine Learning Developer Intern information

See Rockville, MD salary details

$26K

$43.4K

$89.7K

How much do machine learning developer intern jobs pay per year?

As of Sep 14, 2026, the average yearly pay for machine learning developer intern in Rockville, MD is $43,405.00, according to ZipRecruiter salary data. Most workers in this role earn between $33,100.00 and $46,900.00 per year, depending on experience, location, and employer.

What does a machine learning developer intern do?

A Machine Learning Developer Intern assists with developing, testing, and implementing machine learning models and algorithms under the guidance of experienced engineers or data scientists. Their tasks may include data preprocessing, model training, evaluating model performance, and helping deploy models into production environments. Interns often collaborate with team members to solve real-world problems using machine learning techniques and may also assist in researching new methodologies or optimizing existing solutions. This role provides hands-on experience in coding, data analysis, and applying theoretical concepts to practical scenarios.

What are the key skills and qualifications needed to thrive as a machine learning developer intern?

To thrive as a Machine Learning Developer Intern, you need a solid understanding of programming (especially Python), statistics, and machine learning concepts, often supported by coursework or relevant project experience. Familiarity with ML frameworks like TensorFlow or PyTorch, and tools such as Jupyter Notebooks and version control systems like Git, is typically expected. Strong analytical thinking, eagerness to learn, and effective communication help interns contribute to team projects and adapt quickly. These skills are essential for solving real-world problems, collaborating with teams, and building a foundation for a successful career in machine learning.

How do machine learning developer interns typically collaborate with data scientists and engineers during their internship?

Machine Learning Developer Interns often work closely with data scientists to understand the problem domain, gather relevant datasets, and select appropriate models. They also collaborate with software engineers to integrate machine learning solutions into existing systems, ensuring scalability and performance. Regular communication through stand-up meetings, code reviews, and collaborative platforms is common, allowing interns to learn best practices and receive feedback on their work. This teamwork not only enhances technical skills but also provides valuable exposure to real-world deployment and project lifecycle management.

What is the difference between Machine Learning Developer Intern vs Data Scientist Intern?

AspectMachine Learning Developer InternData Scientist Intern
Required CredentialsTypically pursuing or recently completed a degree in Computer Science, Data Science, or related fields; knowledge of programming languages like Python or JavaSimilar educational background; strong skills in statistics, programming, and data analysis
Work EnvironmentHands-on experience with ML models, algorithms, and software development in tech or research settingsData analysis, visualization, and interpretation in business or research contexts
Employer & Industry UsageTech companies, startups, research labs focusing on AI/ML projectsBusiness, finance, healthcare, and research organizations analyzing large datasets

Both roles involve working with data and programming, but Machine Learning Developer Interns focus more on building and deploying ML models, while Data Scientist Interns emphasize data analysis and insights. The roles often overlap, especially in tech environments, but their core tasks differ slightly.

Machine Learning / Federated-Learning Engineer with Security Clearance

Fairfax, VA • On-site

steampunk
IT Services • 201 - 500 employees

Other

Posted 18 days ago


Job description

Overview We are seeking a Machine Learning (ML) / Federated-Learning Engineer responsible for developing, implementing, and supporting machine learning solutions within controlled and distributed environments. This role will support the Bounded Use Case B demonstration through controlled model adaptation, fine-tuning, and federated learning workflows. The ML / Federated-Learning Engineer will work across the machine learning lifecycle to develop and integrate model training and adaptation workflows, support distributed and federated learning capabilities, evaluate model performance, and ensure solutions operate within defined technical and security constraints. This role requires strong hands-on experience with machine learning engineering, model development, and distributed computing environments. Contributions * Design, develop, and implement machine learning solutions supporting the Bounded Use Case B demonstration * Develop and execute controlled model adaptation and fine-tuning workflows based on defined use cases and requirements * Design, implement, and support federated learning workflows that enable distributed model training and adaptation * Develop and maintain machine learning pipelines supporting data preparation, model training, fine-tuning, evaluation, and deployment * Analyze and preprocess data, including feature engineering and transformation, to support machine learning workflows * Configure and optimize machine learning models and training processes to meet defined performance and operational requirements * Evaluate model performance, behavior, and effectiveness using established metrics and validation techniques * Develop processes and controls to ensure model adaptation and training occur within defined technical, security, and operational boundaries * Integrate machine learning capabilities with existing applications, platforms, data sources, and infrastructure * Troubleshoot model training, integration, performance, and distributed learning issues * Develop reusable code, tools, and automation to support machine learning and federated learning workflows * Collaborate with data scientists, software engineers, cloud engineers, cybersecurity teams, and other technical stakeholders to develop and integrate machine learning capabilities * Document machine learning architectures, workflows, configurations, testing results, and technical implementation decisions * Support version control, CI/CD, and other software engineering practices throughout the machine learning development lifecycle * Support an Agile software development lifecycle * Maintain awareness of emerging machine learning, model fine-tuning, federated learning, and distributed AI technologies and practices Qualifications Required: * Ability to obtain and maintain a government security clearance * Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, or a related technical field, or equivalent relevant experience * 5+ years of experience in software engineering, data science, machine learning, AI engineering, or related technical disciplines, including hands-on machine learning engineering experience * Hands-on experience developing, training, fine-tuning, and evaluating machine learning models * Experience designing and implementing machine learning training and inference workflows * Experience with federated learning, distributed machine learning, or distributed model training concepts and architectures * Strong programming experience using Python and common machine learning libraries and frameworks * Experience with machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, or equivalent technologies * Experience with data preprocessing, feature engineering, and model evaluation techniques * Experience developing and maintaining data and machine learning pipelines * Knowledge of model evaluation techniques, performance metrics, and validation methodologies * Experience integrating machine learning models and capabilities into applications or production environments * Understanding of distributed computing concepts and architectures * Knowledge of cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP) * Experience with version control systems such as Git and CI/CD practices * Experience troubleshooting machine learning model, pipeline, and integration issues * Strong analytical, problem-solving, communication, and collaboration skills Preferred: * Hands-on experience implementing federated learning architectures or workflows * Experience with federated learning frameworks or technologies * Experience with large language models (LLMs), foundation models, or other generative AI technologies * Experience with parameter-efficient fine-tuning or other model adaptation techniques * Experience deploying and operating machine learning workloads in cloud environments * Knowledge of MLOps practices, model lifecycle management, and automated ML pipelines * Experience implementing machine learning solutions within controlled, secure, or restricted environments * Experience working within federal government or other highly regulated environments * Relevant cloud, machine learning, or AI certification About steampunk Steampunk relies on several factors to determine salary, including but not limited to geographic location, contractual requirements, education, knowledge, skills, competencies, and experience. The projected compensation range for this position is $115,000 to $150,000. The estimate displayed represents a typical annual salary range for this position. Annual salary is just one aspect of Steampunk's total compensation package for employees. Learn more about additional Steampunk benefits here. Identity Statement As part of the application process, you are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud. Steampunk is a Change Agent in the Federal contracting industry, bringing new thinking to clients in the Homeland, Federal Civilian, Health and DoD sectors. Through our Human-Centered delivery methodology, we are fundamentally changing the expectations our Federal clients have for true shared accountability in solving their toughest mission challenges. If you want to learn more about our story, visit http://www.steampunk.com. We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. Steampunk participates in the E-Verify program.