1

Mlops Engineer Internship Jobs (NOW HIRING)

AI Engineer

Pleasanton, CA ยท On-site

$116K - $159K/yr

Contribute to MLOps workflows including CI/CD, model lifecycle management, observability, and cloud ... Experience working on applied AI projects in academic, internship, or startup settings * Interest ...

Senior Machine Learning Engineer (MLOPS)

Atlanta, GA ยท On-site

$100K - $138K/yr

Qualifications& Requirements: * 6+years of professional experience (or equivalent strong academic/internship experience) inMLOps, Data Engineering, Software Engineering, or a related field. * 3+ ...

... ops, internships, academic, or personal projects) * 1 years of AI/ML engineering experience ... Familiarity with MLOps concepts (experimentation tracking, model packaging, deployment patterns ...

Showing results 41-60

Mlops Engineer Internship information

See salary details

$11

$19

$29

How much do mlops engineer internship jobs pay per hour?

As of Aug 23, 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.

More about Mlops Engineer Internship jobs

What cities are hiring for Mlops Engineer Internship jobs?

Cities with the most Mlops Engineer Internship job openings:

What are the most commonly searched types of Mlops Engineer jobs?

The most popular types of Mlops Engineer jobs are:

What states have the most Mlops Engineer Internship jobs?

States with the most job openings for Mlops Engineer Internship jobs include:

What job categories do people searching Mlops Engineer Internship jobs look for?

The top searched job categories for Mlops Engineer Internship jobs are:

Infographic showing various Mlops Engineer Internship job openings in the United States as of August 2026, with employment types broken down into 94% Full Time, 2% Part Time, and 4% Contract. Highlights an 85% Physical, 6% Hybrid, and 9% Remote job distribution, with an average salary of $40,174 per year, or $19.3 per hour.

AI Engineer

Avathon

Pleasanton, CA โ€ข On-site

$116K - $159K/yr

Full-time

Medical, Retirement

Re-posted 26 days ago


Job description

About the Role

Senior AI Engineer - Generative AI & LLMs

At Avathon, we are building cutting-edge AI solutions that transform operations across asset-intensive industries such as Supply Chain, Logistics, Energy, Mining, Aerospace, and Industrial Manufacturing. As an AI Engineer, you will play a critical role in designing, developing, and deploying scalable AI systems with a strong focus on Generative AI, Large Language Models (LLMs), and production-grade machine learning applications.

This role is ideal for someone with strong engineering depth who can bridge research and production-building robust AI platforms, optimizing LLM workflows, and delivering high-impact solutions across forecasting, route optimization, anomaly detection, predictive maintenance, and intelligent automation.

With 3-5 years of hands-on industry experience, you are expected to bring expertise in AI system design, ML engineering, LLM deployment, and scalable software development within fast-paced startup environments.

You Will
  • Design, build, and deploy production-grade AI/ML systems with strong emphasis on Generative AI and LLM-powered applications
  • Develop and optimize end-to-end LLM pipelines including RAG architectures, fine-tuning, prompt orchestration, evaluation, and observability
  • Build scalable backend services and APIs for AI applications using modern engineering best practices
  • Implement and productionize transformer-based models and GenAI workflows for enterprise use cases
  • Design vector search systems, embedding pipelines, and retrieval frameworks for knowledge-intensive applications
  • Partner closely with Product, Engineering, and Business teams to translate operational challenges into scalable AI solutions
  • Drive experimentation, benchmarking, model evaluation, and performance optimization with scientific rigor
  • Improve inference efficiency, latency optimization, cost management, and reliability of deployed AI systems
  • Establish guardrails, hallucination detection, monitoring, and responsible AI practices for production deployments
  • Contribute to MLOps workflows including CI/CD, model lifecycle management, observability, and cloud deployment
  • Stay current with the latest advancements in LLMs, agentic systems, foundation models, and applied AI engineering
You'll Have
  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field
  • 3-5 years of hands-on industry experience in AI Engineering, Machine Learning Engineering, Applied AI, or related roles
  • Strong experience building and deploying LLM-based applications in production environments
  • Solid expertise with Python and modern AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, or similar
  • Strong understanding of transformer architectures, LLM fine-tuning, prompt engineering, RAG systems, and vector databases
  • Experience building scalable APIs and backend systems supporting AI workflows
  • Familiarity with cloud platforms such as AWS, GCP, or Azure
  • Strong software engineering fundamentals including system design, debugging, performance optimization, and production reliability
  • Experience with containerization, deployment pipelines, and collaborative engineering environments
  • Strong analytical thinking, ownership mindset, and ability to work in ambiguous, fast-moving startup environments
  • Strong communication skills and ability to work cross-functionally with technical and business stakeholder
Preferred Qualifications
  • Exposure to Retrieval-Augmented Generation (RAG), vector databases, or embedding-based search systems
  • Familiarity with LLM observability and evaluation tools (e.g., Langfuse, LangSmith, Arize Phoenix, Weights & Biases)
  • Hands-on experience with practical LLM deployment -- prompt versioning, cost/latency tracking, guardrails, or hallucination detection
  • Exposure to LLM evaluation frameworks (e.g., RAGAS, DeepEval) or LLM-as-judge evaluation patterns
  • Basic understanding of MLOps practices and model lifecycle management
  • Experience working on applied AI projects in academic, internship, or startup settings
  • Interest in industrial AI and asset-intensive environments
  • Industry exposure in one or more of the following domains: Mining, Oil & Gas, Aerospace, Supply Chain, Logistics, or Renewable Energy
Interview Process

As part of the interview process, you will be asked to complete a technical assessment.

Benefits & Perks

What are the benefits and perks at Avathon? Below are some highlights we offer to our U.S. full-time employees -- we'd love to connect and share more!

  • Evolving culture with the opportunity to drive new ideas and technology
  • Stock Option Grants
  • Medical Coverage and Parental Leave Plans
  • 401k with Employer Match
  • Monthly Technology Allowance
  • Newly renovated office space located near Pleasanton, CA -- including fully stocked beverage and snack areas

Contract and temporary roles are not eligible for the above benefits.

Compensation

Pay Range:ย $110k - $130k salary annually. Pay for this position is based on a number of factors including geographic location and may vary depending on job-related knowledge, skills, and experience.

Location: This role is not remote. Candidates must be based in the Bay Area, CA and are expected to report to our Pleasanton office 5 days a week.