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Machine Learning Internship No Experience Jobs (NOW HIRING)

Advantest

Machine Learning Engineer

San Jose, CA · On-site

Advantest is seeking a motivated Junior Machine Learning Engineer to support the development of ... Hands-on experience with ML modeling via coursework, internships, or independent projects.

Motorola

Machine Learning Engineer

Los Angeles, CA · On-site

... no experience required with an advance degree (MS or PhD) * Strong foundation in supervised and unsupervised learning and statistical modeling. * Experience with Python ML frameworks (e.g ...

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Experience in other programming languages (eg. Java, R, Haskell) a plus. * Solid knowledge of machine learning tools (eg. scikit-learn, tensorflow, keras, pytorch, Spark MLlib) required. * Experience ...

NTENT

Machine Learning Engineer

Carlsbad, CA

Experience in other programming languages (eg. Java, R, Haskell) a plus. * Solid knowledge of machine learning tools (eg. scikit-learn, tensorflow, keras, pytorch, Spark MLlib) required. * Experience ...

Full Scope

Machine Learning Engineer

Reston, VA

Proven experience as a Machine Learning Engineer or in a similar role. * Strong proficiency in ... programming languages such as Python, R, or Java. * Experience with machine learning frameworks and ...

Chefman & CHEF iQ

Machine Learning Engineer

Mahwah, NJ · On-site

Required : • Experience developing and deploying machine learning models in production environments. • Strong experience with computer vision, image classification, object detection, deep ...

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How much do machine learning internship no experience jobs pay per year?

As of Jun 7, 2026, the average yearly pay for machine learning internship no experience in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Intern with no prior experience, and why are they important?

To thrive as a Machine Learning Intern with no experience, you need a solid understanding of programming (especially Python), basic statistics, and foundational machine learning concepts, often demonstrated through coursework or personal projects. Familiarity with tools like scikit-learn, TensorFlow, Jupyter Notebooks, and version control systems (e.g., Git) is typically expected. Curiosity, eagerness to learn, problem-solving ability, and effective communication are standout soft skills in this position. These skills and qualities are crucial for adapting quickly, contributing to projects, and maximizing growth in a hands-on learning environment.

What is a machine learning internship with no experience?

A machine learning internship with no experience is an entry-level opportunity designed for students or individuals who are new to the field of machine learning and may not have previous professional experience. These internships typically focus on foundational skills such as data preprocessing, understanding basic algorithms, and using popular tools like Python, TensorFlow, or PyTorch. Interns are often provided with mentorship, training, and real-world projects to help them learn and apply machine learning concepts. The goal is to gain practical experience and build a portfolio, which can be helpful for future job opportunities in the field.

What types of projects or tasks are typically assigned to machine learning interns with no prior experience?

Machine learning interns with no prior experience are often assigned to support tasks such as data preprocessing, exploratory data analysis, and helping to clean or organize datasets. They may also assist with implementing, testing, or tuning basic machine learning models under the guidance of experienced team members. Interns are encouraged to participate in team meetings, contribute to code reviews, and learn about the deployment process, giving them valuable exposure to real-world workflows and collaboration within a machine learning team.

What is the difference between Machine Learning Internship No Experience vs Data Science Intern No Experience?

AspectMachine Learning Internship No ExperienceData Science Intern No Experience
Required CredentialsBasic programming skills, introductory knowledge of ML conceptsBasic programming skills, introductory knowledge of data analysis
Work EnvironmentTech companies, startups, research labsTech companies, consulting firms, research organizations
Employer & Industry UsagePrimarily in AI and ML-focused rolesBroader data analysis and business intelligence roles
Search & Comparison IntentUnderstanding entry-level ML roles for beginnersExploring data analysis internships for beginners

Both internships are entry-level roles requiring foundational skills in programming. Machine Learning Internships focus on developing algorithms and models, while Data Science Internships emphasize data analysis and visualization. The choice depends on your interest in AI/ML versus broader data analysis tasks.

More about Machine Learning Internship No Experience jobs
What cities are hiring for Machine Learning Internship No Experience jobs? Cities with the most Machine Learning Internship No Experience job openings:
What states have the most Machine Learning Internship No Experience jobs? States with the most job openings for Machine Learning Internship No Experience jobs include:
What job categories do people searching Machine Learning Internship No Experience jobs look for? The top searched job categories for Machine Learning Internship No Experience jobs are:
Machine Learning Internship No Experience jobs near you

Machine Learning Engineer

Advantest

San Jose, CA • On-site

Full-time

Posted 12 days ago

Job description

Advantest is seeking a motivated Junior Machine Learning Engineer to support the development of data-driven and ML-powered solutions for semiconductor R&D, test, and operations teams. In this role, you'll contribute to building predictive models, conducting statistical analyses, and assisting in the development of light-to-moderate data pipelines that help transform complex semiconductor datasets into actionable insights.
This position is ideal for a recent graduate with strong foundational ML skills who is eager to learn, collaborate, and grow in a fast-paced, technically rich environment. You'll work alongside experienced engineers, data scientists, and domain experts while gaining hands-on experience across the ML lifecycle-from data preparation to model deployment
Key Responsibilities
Machine Learning & Advanced Analytics
  • Develop and evaluate ML models (e.g., classification, anomaly detection, regression, time-series analysis).
  • Perform feature engineering and exploratory data analysis on semiconductor datasets.
  • Contribute to model deployment workflows in collaboration with ML data scientists, following MLOps best practices.
  • Assist in implementing model monitoring, retraining workflows, and documentation.
  • Experiment with modern analytics techniques, including LLM-based or generative-AI methods, under guidance from senior team members.

Data Engineering & Pipeline Support
  • Help build and maintain ETL/ELT workflows that prepare data for analysis and modeling.
  • Support data quality checks, versioning, and data validation tasks.
  • Work with cloud and on-prem tools to help ensure data accessibility for ML applications.

Cross-Functional Collaboration
  • Work with semiconductor engineers and data scientists to translate domain challenges into analytical tasks.
  • Support the creation of dashboards, reports, and visualizations that communicate insights clearly.
  • Learn and apply semiconductor-specific data concepts with the support of senior mentors.

Education and Experience
  • B.S. or M.S. in Computer Science, Data Science, Engineering, Applied Math, or a related quantitative field.
  • Hands-on experience with ML modeling via coursework, internships, or independent projects.
  • Exposure to data engineering concepts-coursework or project-based experience is acceptable.

Required Technical Skills
  • Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).
  • Familiarity with Pandas, NumPy, and basic data manipulation tools.
  • Understanding of API development concepts.
  • Exposure to containerization (e.g., Docker) and Linux environments.
  • Experience with dashboarding or visualization tools (Power BI, Tableau, Dash, etc.).
  • Familiarity with DevOps principles and tools.

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