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Internship Deep Reinforcement Learning Jobs in Ohio

Nestle

Manager, Learning & Development

Solon, OH · On-site

... reinforcement tools-in partnership with HRBPs, L&D peers, site and functional leaders, and ... deep expertise, and ensuring consistent integration across leader groups and development ...

STR

Lead Algorithm & Signal Processing Engineer

Dayton, OH · On-site

$157K - $220K/yr

Machine learning tasks such as natural language processing, image recognition, semantic segmentation, reinforcement learning, approaches such as Bayesian, deep convolutional and graph neural network ...

STR

Lead Algorithm & Signal Processing Engineer

Dayton, OH · On-site

$157K - $220K/yr

Machine learning tasks such as natural language processing, image recognition, semantic segmentation, reinforcement learning, approaches such as Bayesian, deep convolutional and graph neural network ...

Procter & Gamble

Data Scientist

Cincinnati, OH · On-site

$85K - $122K/yr

Utilizing your expertise in Operations Research (including optimization and simulation) and machine learning models (such as tree models, deep learning, and reinforcement learning), you will directly ...

Penske

Staff Data Scientist

Beachwood, OH · On-site

Select and implement appropriate modeling techniques, including classical ML, deep learning, generative AI, and reinforcement learning where applicable. * Oversee the full model lifecycle: data ...

Proctor & Gamble

Data Scientist

Cincinnati, OH · On-site

$85K - $122K/yr

Utilizing your expertise in Operations Research (including optimization and simulation) and machine learning models (such as tree models, deep learning, and reinforcement learning), you will directly ...

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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?

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 popular job titles related to Internship Deep Reinforcement Learning jobs in Ohio? For Internship Deep Reinforcement Learning jobs in Ohio, the most frequently searched job titles are:
What job categories do people searching Internship Deep Reinforcement Learning jobs in Ohio look for? The top searched job categories for Internship Deep Reinforcement Learning jobs in Ohio are:
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Internship Deep Reinforcement Learning jobs near you
Infographic showing various Internship Deep Reinforcement Learning job openings in Ohio as of August 2026, with employment types broken down into 1% As Needed, 67% Full Time, 29% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Senior Machine Learning Engineer (Reinforcement Learning/World Model)

Path Robotics

Columbus, OH • On-site, Remote

$100K - $138K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 23 days ago

Job description

Build the Path Forward
At Path Robotics, we're building the future of embodied intelligence. Our AI-driven systems enable robots to adapt, learn, and perform in the real world closing the skilled labor gap and transforming industries. We go beyond traditional methods, combining perception, reasoning, and control to deliver field-ready AI that is risk-aware, reliable, and continuously improving through real-world use.
Big, hard problems are our everyday work, and our team of intelligent, humble, and driven people make the impossible possible together.
Manufacturing demands exceptionally high performance, reliability, and adaptability. Processes like welding involve fast, complex, and poorly modeled physics that traditional simulators struggle to capture - especially in the long tail of real-world conditions.
We are building intelligent robotic systems that learn directly from data by combining neural world models with reinforcement learning. Our goal is to give robots the ability to learn, predict, and plan in complex manufacturing environments by replacing or augmenting classical physics simulators with fast, high-fidelity learned ones.
We are seeking a Senior Machine Learning Engineer to lead the development of a neural welding simulator - a learned world model that captures the visual and physical dynamics of welding and enables large-scale RL training. This role sits at the intersection of generative modeling, robotics, and applied physics. It is research-heavy by design, while still grounded in production reality.
We are hiring two Senior Machine Learning Engineers with complementary specializations:
  • Senior Machine Learning Engineer, World Models
  • Senior Machine Learning Engineer, Reinforcement Learning
Senior ML Engineer, World Models
What You'll Do
  • Build action-conditioned world models that predict how the welding process evolves under changes to robot motion and process parameters.
  • Model relationships among inputs, system state, physical dynamics, and resulting weld quality.
  • Develop multimodal models using data such as video, 3D scans, thermal measurements, electrical signals, robot state, and process parameters.
  • Explore latent dynamics, video prediction, generative modeling, and spatiotemporal representations.
  • Improve long-horizon rollout accuracy, physical plausibility, temporal consistency, and computational efficiency.
  • Quantify model uncertainty and identify conditions under which predictions are unreliable.
  • Validate learned predictions against real-world welding data.
  • Integrate the model into RL, planning, process-optimization, evaluation, and synthetic-data workflows.
  • Prevent downstream optimization systems from exploiting inaccuracies in the learned model.
  • Translate promising research into scalable training and inference systems.
Senior ML Engineer, Reinforcement Learning
What You'll Do
  • Develop reinforcement learning approaches for optimizing welding decisions and process outcomes.
  • Define state, observation, action, and reward representations based on measurable manufacturing objectives.
  • Train and evaluate policies using learned world models, traditional simulation, offline datasets, and controlled real-world experiments.
  • Develop offline, model-based, or constrained RL methods suitable for limited and expensive physical interaction.
  • Optimize across competing objectives such as weld quality, cycle time, reliability, energy use, and equipment constraints.
  • Design methods that account for uncertainty, distribution shift, delayed outcomes, and sparse or imperfect reward signals.
  • Diagnose reward exploitation, unsafe behavior, policy instability, and model exploitation.
  • Establish reliable offline and real-world policy evaluation methods.
  • Partner with controls, welding, robotics, world-model, data, and ML infrastructure engineers.
  • Translate research prototypes into dependable training, evaluation, and deployment systems.
Who You Are
  • Master's or PhD in Computer Science, Robotics, Machine Learning, or related field, or equivalent practical experience.
  • Experience developing and deploying reinforcement learning algorithms on real-world systems.
  • Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
  • Experience with simulation environments (e.g., MuJoCo, Isaac Gym).
  • Solid understanding of probability, statistics, and optimization.
  • Experience with training and deploying ML models in production systems.
Why You'll Love It Here
  • Daily free lunch to keep you fueled and connected with the team
  • Flexible PTO so you can take the time you need, when you need it
  • Comprehensive medical, dental, and vision coverage
  • 6 weeks fully paid parental leave, plus an additional 6-8 weeks for birthing parents (12-14 weeks total)
  • 401(k) retirement plan through Empower
  • Generous employee referral bonuses-help us grow our team!
Who We Are
At Path Robotics we love coming to work to solve interesting and tough challenges but also because our ideas are welcomed and valued. We encourage unique thinking and are dedicated to creating a diverse and inclusive environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
If you require a reasonable accommodation to participate in the application process or any part of the hiring process, please contact HR@path-robotics.com. We are committed to providing equal access and will work with qualified individuals to ensure a fair and accessible hiring experience. We will respond to your request within 48 hours.

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