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Machine Learning Unpaid Internship Jobs in Michigan

Corning

... on academic, internship, personal, or professional projects. - Strong Python foundation and hands-on experience with at least one machine learning library or framework such as scikit-learn ...

Mariana Minerals

Machine Learning Engineer

Ann Arbor, MI · On-site

$120K - $180K/yr

Desired Qualifications * 2-8+ years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing-or a strong recent graduate with ...

Quora

$139K - $168K/yr

At Poe, we use Machine Learning in various parts of the product - bot routing, agent flow, code ... Previous software engineering experience via an internship, work experience, or coding competition

Quora

$139K - $168K/yr

At Poe, we use Machine Learning in various parts of the product - bot routing, agent flow, code ... Previous software engineering experience via an internship, work experience, or coding competition

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Machine Learning Unpaid Internship information

What is a machine learning unpaid internship?

A Machine Learning Unpaid Internship is a temporary position where students or recent graduates work with professionals to gain practical experience in machine learning without receiving monetary compensation. Interns typically assist with data analysis, model development, and research tasks, while learning about real-world machine learning applications. These internships help individuals build relevant skills, expand their professional network, and improve their resumes for future job opportunities.

What is the difference between Machine Learning Unpaid Internship vs Data Science Intern?

AspectMachine Learning Unpaid InternshipData Science Intern
Required CredentialsBasic programming, coursework in ML or AIStatistics, programming, data analysis skills
Work EnvironmentStartups, tech companies, research labsTech firms, consulting agencies, research institutions
Industry UsageFocus on ML model development and algorithmsBroader data analysis, visualization, reporting

The main difference is that a Machine Learning Unpaid Internship emphasizes developing ML models and algorithms, often requiring knowledge of programming and AI concepts. In contrast, a Data Science Intern role covers a wider range of data analysis tasks, including visualization and reporting. Both roles are common in tech and research environments, but the internship focus and skill requirements differ slightly.

What are the key skills and qualifications needed to thrive as a machine learning unpaid intern, and why are they important?

To thrive as a Machine Learning Unpaid Intern, you should have a solid understanding of linear algebra, statistics, and programming languages like Python, along with coursework or experience in machine learning concepts. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is typically expected. Strong problem-solving skills, eagerness to learn, and effective communication set standout interns apart. These skills are essential for contributing to projects, adapting quickly in a dynamic field, and collaborating with team members on real-world machine learning challenges.

What kinds of projects and responsibilities can I expect during a machine learning unpaid internship?

As a machine learning unpaid intern, you will typically work on projects such as data preprocessing, model training, and performance evaluation under the guidance of experienced team members. Your daily tasks may include cleaning datasets, implementing algorithms, and conducting experiments to test model improvements. Interns often collaborate with data scientists and engineers, participating in team meetings and code reviews to learn best practices. This hands-on experience provides valuable exposure to real-world machine learning workflows and tools, helping you build skills that are essential for future roles in the field.
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Machine Learning Unpaid Internship jobs near you

Machine Learning Engineer

Mariana Minerals

Ann Arbor, MI • On-site

Full-time

Re-posted yesterday

Job description

Job Summary:
Mariana Minerals is a software-first, vertically integrated minerals company focused on supplying critical minerals for modern energy and technology. They are seeking a Machine Learning Engineer to develop and improve machine learning systems for mineral refining facilities, working with real data to enhance operational efficiency.
Responsibilities:
• Run reinforcement learning experiments in our physically realistic simulators of mineral processing operations, and help turn the results into better controllers.
• Build and refine pieces of our training environments—reward functions, observations, and action logic—with guidance from senior engineers.
• Train control models, track and interpret their performance, and dig into why a model underperforms.
• Help close the gap between simulation and reality by comparing model behavior against real plant data and flagging where the physics diverges.
• Write clean, well-tested code and contribute to the services that put models into production.
• Partner with process and chemistry experts to understand the unit operations you're modeling.
Qualifications:
Required:
• 0–4 years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing—or a strong recent graduate with demonstrated project depth.
• Solid grounding in machine learning fundamentals, with working knowledge of modern deep learning; exposure to reinforcement learning is a strong plus.
• Proficiency in Python and comfort reading and debugging an existing codebase.
• Curiosity about physical, industrial systems and eagerness to learn chemistry and process engineering from experts who will challenge your assumptions.
• A self-starter who asks good questions, ships, and escalates blockers early.
Company:
Mariana Minerals develops mineral projects using technology to supply critical minerals for energy, AI, and defense applications. Founded in 2022, the company is headquartered in Houston, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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