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Internship Deep Reinforcement Learning Jobs (NOW HIRING)

Jobgether

The role combines deep theoretical research with hands-on system development and experimentation ... Conduct research on reinforcement learning methods for multi-modal systems, including diffusion ...

Rogo

AI Researcher

Manhattan, NY · On-site

... with deep reinforcement learning in any context (autonomous vehicles, robotics, or LLMs) • Experience working with data generated by human experts for model training • Financial services ...

Starfish

Software Developer

Colorado Springs, CO

The selected candidate will drive the design, development, and integration of innovative Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) capabilities into defense and mission ...

Centific

Applied Reinforcement Learning Engineer

Applied Reinforcement Learning Engineer Location: Palo Alto, CA or Seattle, WA (Hybrid/Remote ... This role requires deep expertise in both classical RL methodologies and modern LLM-based agent ...

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How much do internship deep reinforcement learning jobs pay per hour?

As of May 31, 2026, the average hourly pay for internship deep reinforcement learning in the United States is $17.04, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $19.23 per hour, depending on experience, location, and employer.

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 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 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.

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Internship Deep Reinforcement Learning jobs near you
Infographic showing various Internship Deep Reinforcement Learning job openings in the United States as of May 2026, with employment types broken down into 10% Full Time, 80% Part Time, and 10% Contract. Highlights an 79% Physical, 1% Hybrid, and 20% Remote job distribution, with an average salary of $35,436 per year, or $17 per hour.
Reinforcement Learning Engineer, Whole Body Controls, Optimus

Reinforcement Learning Engineer, Whole Body Controls, Optimus

Tesla

Palo Alto, CA • On-site

$98.30K - $127.10K/yr

Full-time

Posted 19 days ago

Tesla rating

8.5

Company rating: 8.5 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

1st of 44 rated automakers

Job description

Job Summary:
Tesla is focused on solving robust embodied intelligence through humanoid robots, and they are seeking a Reinforcement Learning Engineer to develop cutting-edge policy learning algorithms. The role involves creating end-to-end reinforcement-learning policies for whole-body movements and evaluating these policies in both simulation and real-world settings.
Responsibilities:
• Develop end-to-end reinforcement-learning policies for whole-body movements
• Design observations, actions, and rewards based on first principles and deep physics understanding
• Develop techniques to improve sim2real transfer, including classical modeling techniques
• Evaluate policies both in simulation and on hardware
• Ship production-quality policies to a fleet of bots
Qualifications:
Required:
• Experience writing production-quality python (including numpy and pytorch)
• Solid understanding of robotics fundamentals, including geometry, linear algebra, kinematics, dynamics, probability, and statistics
• Familiarity with Machine learning and Reinforcement Learning fundamentals OR strong background in optimization-based planning and control
• Experience working with robotic systems, ideally on legged robotic systems with high degrees of freedom
• Experience with sim2real techniques OR deep understanding of physics fundamentals
• Experience implementing control strategies including impedance control, adaptive control, force control, MPC on hardware preferred
Preferred:
• Experience implementing control strategies including impedance control, adaptive control, force control, MPC on hardware
Company:
Tesla is an electric vehicle and clean energy company that provides electric cars, solar, and renewable energy solutions. Founded in 2003, the company is headquartered in Austin, USA, with a team of 10001+ employees. The company is currently Late Stage.

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