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Mlops Engineer Internship Jobs (NOW HIRING)

Socket.dev

Junior AI/ML Engineer

Auburn Hills, MI · On-site

Support preparation of solutions for operationalization in partnership with MLOps teams Growth ... Internship or project experience deploying ML in real systems * Exposure to cloud-based data or ML ...

Copart, Inc.

DevOps Engineering Intern

Dallas, TX · On-site

AI / MLOps Support * Assist with deployment and monitoring of machine learning and AI applications ... Personal, academic, or internship projects involving Kubernetes or cloud deployments. * Experience ...

Pangram

Machine Learning Engineer (Junior)

New York, NY · On-site

$135K - $150K/yr

Practical experience with deep learning: internships, undergrad or masters' level research projects ... Experience with MLOps and experiment tracking * Experience with DevOps tools * Familiarity with ...

Cotiviti

Senior AI Engineer

$128K - $164K/yr

Working closely with the CDI (Clinical Document Improvement) Lead, Data Scientists, MLOps Engineers ... internships, or work experience. * Familiarity with containerization (Docker) and cloud ...

Cotiviti

Senior AI Engineer

$128K - $164K/yr

Working closely with the CDI (Clinical Document Improvement) Lead, Data Scientists, MLOps Engineers ... internships, or work experience. * Familiarity with containerization (Docker) and cloud ...

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How much do mlops engineer internship jobs pay per hour?

As of Sep 13, 2026, the average hourly pay for mlops engineer internship in the United States is $19.31, according to ZipRecruiter salary data. Most workers in this role earn between $16.11 and $20.91 per hour, depending on experience, location, and employer.

What is an MLOps engineer internship?

An MLOps Engineer Internship is a temporary position designed for students or recent graduates to gain hands-on experience in the field of Machine Learning Operations (MLOps). Interns typically work alongside experienced engineers to help streamline and automate the process of deploying, monitoring, and maintaining machine learning models in production environments. The internship provides valuable exposure to tools and practices such as CI/CD for ML, containerization, model versioning, and cloud platforms. This role is ideal for those looking to bridge the gap between data science and software engineering, gaining practical skills in both areas. Interns often contribute to real-world projects and learn about best practices in scaling and operationalizing AI solutions.

What are some typical projects or tasks I might work on during an MLOps engineer internship?

As an MLOps Engineer Intern, you can expect to work on tasks such as automating machine learning model deployment pipelines, setting up continuous integration/continuous deployment (CI/CD) workflows, and monitoring models in production. You may also assist with optimizing infrastructure for machine learning workloads, ensuring reproducibility of experiments, and collaborating closely with data scientists and software engineers. These projects are designed to give you hands-on experience with real-world MLOps tools and practices, preparing you for a full-time role in the field.

What are the key skills and qualifications needed to thrive as an MLOps engineer intern, and why are they important?

To thrive as an MLOps Engineer Intern, a strong foundation in machine learning concepts, programming (Python, Bash), and familiarity with cloud platforms is essential, often backed by studies in computer science or a related field. Experience with tools such as Docker, Kubernetes, CI/CD pipelines, and version control systems like Git is typically required. Strong problem-solving skills, collaboration, and adaptability help interns navigate technical challenges and team environments. These skills and qualities are crucial for efficiently deploying, maintaining, and scaling machine learning models in production settings.

What is the difference between Mlops Engineer Internship vs Data Engineer Internship?

AspectMlops Engineer InternshipData Engineer Internship
Required CredentialsBasic knowledge of machine learning, cloud platforms, scriptingStrong SQL, programming, data modeling skills
Work EnvironmentTech companies, startups, cloud service providersData-centric teams, analytics firms, tech companies
Industry UsageAI/ML projects, deployment pipelinesData pipelines, database management
Search & Comparison IntentUnderstanding roles in ML deploymentUnderstanding data infrastructure roles

The comparison between Mlops Engineer Internship and Data Engineer Internship highlights that both roles involve working with data and cloud technologies but focus on different aspects. Mlops internships emphasize deploying and maintaining machine learning models, while Data Engineer internships focus on building data pipelines and infrastructure. Candidates should choose based on their interest in ML deployment versus data management.

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Mlops Engineer Internship jobs near you
Infographic showing various Mlops Engineer Internship job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 89% Full Time, 7% Part Time, and 3% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution, with an average salary of $40,174 per year, or $19.3 per hour.

Junior AI/ML Engineer

Auburn Hills, MI • On-site

Socket.dev
Network Security • 1 - 10 employees
Learn more about Socket.devexternal

Other

Posted 7 days ago

Job description

Role Summary:

The Junior AI/ML Engineer contributes to the design, build, and delivery of end-to-end AI/ML solutions under the guidance of senior engineers. This role is engineering-first, applying data science and machine learning as tools within well-engineered software systems.

Engineers at this level focus on well-defined implementation tasks within a larger solution, learning the full lifecycle — design, development, validation, and production handoff — through pairing, code review, and structured mentoring.

AI & ML Development:

  • Implement ML models and components against established designs, using structured, time-series, and unstructured data

  • Run and document model validation, evaluation, and error analysis under senior guidance

  • Build familiarity with the team's AI/ML techniques and how they are applied to engineering, quality, and product use cases

Software & Systems Engineering:

  • Contribute production-quality code to AI systems, including:

  • Data pipelines and feature engineering

  • Model training and inference services

  • Components of agentic solutions combining LLM and other systems

  • Write clean, maintainable, and testable code (primarily Python), responding constructively to code review

  • Use the team's shared AI/ML components and engineering frameworks

Delivery & Execution:

  • Deliver well-scoped implementation tasks reliably, escalating blockers early

  • Participate in requirement clarification and solution iteration with the team

  • Support preparation of solutions for operationalization in partnership with MLOps teams

Growth Expectations:

  • Progress toward independent ownership of implementation tasks end-to-end

  • Develop breadth across data, modeling, and software concerns

  • Actively seek and apply feedback from senior engineers

Qualifications Basic Qualifications:
  • Bachelor's degree in engineering, computer science, applied mathematics, or a related field
  • A minimum of 1 year of experience

  • Solid software engineering fundamentals

  • Exposure to machine learning through coursework, internships, or projects

  • Proficiency in Python; familiarity with common ML libraries

  • Willingness to work across data, modeling, and software concerns

Preferred Qualifications:

  • Internship or project experience deploying ML in real systems

  • Exposure to cloud-based data or ML platforms

  • Interest in LLM-based and agentic solutions

  • Familiarity with software delivery practices (version control, CI, testing)

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