ZipRecruiter

Log In

1

Internship Deep Reinforcement Learning Jobs in Michigan

General Motors

Machine Learning for Robotics

Warren, MI · On-site

Reinforcement learning (RL) * Imitation learning and learning from demonstration * Deep learning methods for perception, planning, and control * Apply learning-based approaches to challenging robotic ...

General Motors

Machine Learning for Robotics

Warren, MI · On-site

Reinforcement learning (RL) * Imitation learning and learning from demonstration * Deep learning methods for perception, planning, and control * Apply learning-based approaches to challenging robotic ...

Tata Consultancy Services

From visual perception and SLAM to multimodal sensor fusion and reinforcement learning, you'll be ... Design, train, and optimize deep neural networks using frameworks such as PyTorch or TensorFlow.

next page

Showing results 1-20

All JobsDeep Reinforcement Learning JobsInternship Deep Reinforcement Learning JobsInternship Deep Reinforcement Learning Jobs in Michigan

Internship Deep Reinforcement Learning information

What types of projects or tasks can I expect to work on during a Deep Reinforcement Learning internship?

As a Deep Reinforcement Learning (DRL) intern, you'll typically work on projects involving the development, implementation, and evaluation of reinforcement learning algorithms. This might include tasks like training agents in simulated environments, tuning hyperparameters, analyzing performance metrics, and collaborating with team members to integrate DRL solutions into larger systems. You'll also likely spend time reading recent research papers, experimenting with frameworks such as TensorFlow or PyTorch, and presenting your findings to the research team. Collaboration with mentors and other interns is common, and you'll gain hands-on experience that prepares you for more advanced roles in AI research or engineering.

What is an internship in Deep Reinforcement Learning?

An internship in Deep Reinforcement Learning (DRL) is a temporary, hands-on position where interns learn and apply state-of-the-art machine learning algorithms that enable computers to learn decision-making tasks through trial and error. Interns typically work on projects involving neural networks, reward systems, and environments like games or simulations. These internships provide valuable experience with frameworks such as TensorFlow or PyTorch, and exposure to current research in artificial intelligence. The experience helps students or recent graduates build technical skills and prepare for careers in AI research or industry.

What are the key skills and qualifications needed to thrive as an Intern in Deep Reinforcement Learning, and why are they important?

To thrive as an Intern in Deep Reinforcement Learning, you need a solid background in mathematics (especially linear algebra, probability, and calculus), programming (Python), and foundational knowledge in machine learning principles, usually supported by ongoing or completed coursework in computer science or related fields. Familiarity with frameworks and tools such as TensorFlow, PyTorch, OpenAI Gym, and experience using version control systems like Git are typically required. Analytical thinking, curiosity, and effective communication are essential soft skills for collaborating on research problems and sharing complex findings. These skills and qualities are crucial for contributing to innovative projects and successfully navigating the challenges of cutting-edge AI research.

What is the difference between Internship Deep Reinforcement Learning vs Data Science Intern?

AspectInternship Deep Reinforcement LearningData Science Intern
Required SkillsMachine learning, programming (Python), reinforcement learning conceptsStatistics, data analysis, programming (Python/R), data visualization
Work EnvironmentResearch labs, AI companies, tech startupsBusiness analytics, tech firms, consulting agencies
Industry UsageAI research, robotics, autonomous systemsBusiness intelligence, marketing, finance

Internship Deep Reinforcement Learning focuses on developing algorithms that enable systems to learn through trial and error, often in AI research or robotics. Data Science Internships involve analyzing data to extract insights and support decision-making. While both roles require programming skills, reinforcement learning emphasizes AI-specific techniques, whereas data science centers on statistical analysis and data visualization.

What are the most commonly searched types of Deep Reinforcement Learning jobs in Michigan? The most popular types of Deep Reinforcement Learning jobs in Michigan are:
What are popular job titles related to Internship Deep Reinforcement Learning jobs in Michigan? For Internship Deep Reinforcement Learning jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Internship Deep Reinforcement Learning jobs in Michigan look for? The top searched job categories for Internship Deep Reinforcement Learning jobs in Michigan are:
What cities in Michigan are hiring for Internship Deep Reinforcement Learning jobs? Cities in Michigan with the most Internship Deep Reinforcement Learning job openings:
Internship Deep Reinforcement Learning jobs near you

Machine Learning Engineer

Mariana Minerals

Ann Arbor, MI • On-site

Full-time

Posted 18 days ago

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 is a software-first critical minerals company that supplies materials essential for modern energy. Founded in 2022, the company is headquartered in Houston, USA, with a team of 51-200 employees. The company is currently Growth Stage.

Apply