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Summer Artificial Intelligence Machine Learning Jobs in Michigan

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Have a graduate degree (masters or PhD) in artificial intelligence, machine learning, operations research or equivalent self study and experience * Have strong programming skills in Python and ...

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Have a graduate degree (masters or PhD) in artificial intelligence, machine learning, operations research or equivalent self study and experience * Have strong programming skills in Python and ...

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Have a graduate degree (masters or PhD) in artificial intelligence, machine learning, operations research or equivalent self study and experience * Have strong programming skills in Python and ...

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Have a graduate degree (masters or PhD) in artificial intelligence, machine learning, operations research or equivalent self study and experience * Have strong programming skills in Python and ...

Instacart

Have a graduate degree (masters or PhD) in artificial intelligence, machine learning, operations research or equivalent self study and experience * Have strong programming skills in Python and ...

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Summer Artificial Intelligence Machine Learning information

What types of projects can I expect to work on during a Summer Artificial Intelligence Machine Learning internship?

As a Summer Artificial Intelligence Machine Learning intern, you can expect to contribute to projects involving data preprocessing, model training, and evaluation under the guidance of experienced mentors. Typical tasks may include developing and testing new algorithms, analyzing large datasets, and supporting the deployment of machine learning models. You'll often collaborate closely with data scientists, software engineers, and other interns, gaining exposure to real-world AI challenges and industry-standard tools. This hands-on experience not only builds technical skills but also improves teamwork and communication abilities, laying a strong foundation for a future career in AI and ML.

What are the key skills and qualifications needed to thrive as a Summer Artificial Intelligence Machine Learning intern, and why are they important?

To thrive as a Summer Artificial Intelligence Machine Learning intern, you need a solid background in mathematics, statistics, and programming (often Python), typically supported by coursework or projects in machine learning or data science. Familiarity with technical tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is commonly expected. Strong problem-solving skills, curiosity, and the ability to collaborate effectively within diverse teams are valuable soft skills for this role. These competencies are crucial for successfully developing, implementing, and refining AI models in a fast-paced, innovation-driven environment.

What is a Summer Artificial Intelligence Machine Learning?

A Summer Artificial Intelligence (AI) Machine Learning (ML) job is a temporary internship or position, typically offered to students or recent graduates during the summer months, that focuses on working with AI and ML technologies. In these roles, participants gain hands-on experience by collaborating on projects involving data analysis, developing machine learning models, and implementing AI algorithms. These positions are designed to help individuals build practical skills, expand their technical knowledge, and explore potential career paths in the rapidly growing field of AI and ML.

What is the difference between Summer Artificial Intelligence Machine Learning vs Summer Data Science?

AspectSummer Artificial Intelligence Machine LearningSummer Data Science
Required CredentialsBachelor's or Master's in CS, AI, ML, or related fieldsBachelor's or Master's in CS, Data Science, Statistics, or related fields
Work EnvironmentResearch labs, tech companies, startupsData analysis teams, consulting firms, tech companies
Industry UsageDeveloping AI models, ML algorithms, automationData analysis, visualization, business insights
Common Search/ComparisonYesYes

Summer Artificial Intelligence Machine Learning focuses on developing AI systems and algorithms, often involving programming and model training. Summer Data Science emphasizes analyzing and interpreting data to generate insights. While both roles require strong technical skills and similar educational backgrounds, AI/ML roles are more research and development-oriented, whereas Data Science roles focus on data analysis and visualization.

What are the most commonly searched types of Artificial Intelligence Machine Learning jobs in Michigan? The most popular types of Artificial Intelligence Machine Learning jobs in Michigan are:
What are popular job titles related to Summer Artificial Intelligence Machine Learning jobs in Michigan? For Summer Artificial Intelligence Machine Learning jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Summer Artificial Intelligence Machine Learning jobs in Michigan look for? The top searched job categories for Summer Artificial Intelligence Machine Learning jobs in Michigan are:
What cities in Michigan are hiring for Summer Artificial Intelligence Machine Learning jobs? Cities in Michigan with the most Summer Artificial Intelligence Machine Learning job openings:
Summer Artificial Intelligence Machine Learning jobs near you
Infographic showing various Summer Artificial Intelligence Machine Learning job openings in Michigan as of August 2026, with employment types broken down into 1% As Needed, 71% Full Time, 25% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Data Scientist (ARTIFICIAL INTELLIGENCE/MACHINE LEARNING)

Defense Logistics Agency

Battle Creek, MI

Full-time

Posted yesterday

New

Defense Logistics Agency rating

8.3

Company rating: 8.3 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

274th of 842 rated public administrative organizations

Job description

Telework Eligible

Yes

Major Duties

  • Serves as an Al (Artificial Intelligence) Testing and Evaluation Data Scientist providing subject matter Al expertise and execution to test and evaluate Al systems.
  • Provides professional and scientific expertise in the application of data science disciplines for complex studies in machine learning and deep learning algorithms, statistical analysis, visualizations, programing and computer science.
  • Provides expertise in evaluating and measuring data science and artificial intelligence systems in accordance with data science Lifecyle practices within DoD parameters.
  • Ensures Al systems are developed in accordance with applicable legal frameworks such as General Data Protection Regulation (GDPR) and National Institute of Standards and Technology (NIST).
  • Incorporates, Office of Management and Budget (0MB) test and evaluation requirements, DOD Al ethical principles into DLA Al test and evaluation framework to generate an RAI test strategy for modeling cases.
  • Utilizes expertise to coordinate the integration of the DLA Al testing and evaluation program during the product lifecycle from concept and design inception, as development proceeds, and integrated to ensure effective RAI implementation.

Qualification Summary

To qualify for a Data Scientist (Artificial Intelligence/Machine Learning), your resume and supporting documentation must support: A. Degree: Mathematics, statistics, computer science, data science or field directly related to the position. The degree must be in a major field of study (at least at the baccalaureate level) that is appropriate for the position. B. Specialized Experience: One year of specialized experience that equipped you with the particular competencies to successfully perform the duties of the position and is directly in or related to this position. To qualify at the GS-14 level, applicants must possess one year of specialized experience equivalent to the GS-13 level or equivalent under other pay systems in the Federal service, military, or private sector. Applicants must meet eligibility requirements including time-in-grade (General Schedule (GS) positions only), time-after­competitive appointment, minimum qualifications, and any other regulatory requirements by the cut-off/closing date of the announcement. Creditable specialized experience includes: Conducts large, agency wide, research and development reviews of metrics, measurements, and evaluation methods for emerging and existing areas of Al. Utilizes data science expertise to develop algorithms and tools to support data manipulation and processing as well as the use of data visualization techniques to articulate high risk findings. Ensures the Al systems are designed for auditability to manage Al risk assessment policies and principles which guide automated decisions supporting DLA business operations. Provides expert advice to senior leadership and Al stakeholders to adopt new or revised policy and implementation plans resulting from Al test and evaluation integration. Assesses data quality and establishes standards to validate quality criteria for data to ensure it meets the necessary requirements for Al testing and evaluation. Experience refers to paid and unpaid experience, including volunteer work done through National Service programs (e.g., Peace Corps, AmeriCorps) and other organizations (e.g., professional, philanthropic, religious, spiritual, community, student, social). Volunteer work helps build critical competencies, knowledge, and skills and can provide valuable training and experience that translates directly to paid employment. You will receive credit for all qualifying experience, including volunteer experience.

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