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Internship Deep Reinforcement Learning Jobs (NOW HIRING)

Apptronik

Senior Reinforcement Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving ... This engineer will leverage their deep expertise in RL to solve critical locomotion and ...

Apptronik

Reinforcement Learning Engineer

Austin, TX · On-site

A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques. * A strong ...

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

As of Sep 12, 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 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 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 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.

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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 September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $35,436 per year, or $17 per hour.

Research Engineer, Reinforcement Learning

San Francisco, CA • On-site

TensorStax (Acq. by Snowflake)
See more jobs at TensorStax (Acq. by Snowflake)external

Full-time

Re-posted 4 days ago

Job description

Job Summary:
TensorStax is building fully autonomous AI systems to manage and maintain mission-critical data infrastructure and pipelines. They are seeking a Research Engineer specializing in Reinforcement Learning to develop and refine reward functions, create RL gym environments, and fine-tune language models using advanced reinforcement learning techniques.
Responsibilities:
• Develop and refine reward functions to optimize agent behavior for complex data engineering tasks.
• Create RL gym environments for language model agents.
• Fine-tune language models using reinforcement learning techniques such as PPO, DPO, and KTO.
• Stay at the forefront of research on RL for language models, incorporating advancements like GRPO, SWE-Gym, and SWE-RL into practical applications.
• Curate and build high-quality datasets for supervised fine-tuning (SFT) and RLHF.
• Design experiments to evaluate and improve the agentic capabilities of language models in data environments.
Qualifications:
Required:
• Deep understanding of reinforcement learning, reward shaping, and optimization strategies.
• Strong familiarity with LLM fine-tuning techniques (PPO, DPO, KTO) and their applications in reinforcement learning.
• Knowledge of recent advancements in RL for language models (GRPO, SWE-Gym, SWE-RL).
• Experience curating and constructing high-quality datasets for fine-tuning.
• Strong problem-solving skills and a history of working on complex ML projects.
• High agency—ability to work independently, experiment proactively, and drive research initiatives forward.
Preferred:
• Experience with distributed training in PyTorch (DDP, FSDP).
• Hands-on experience designing RL environments for traditional RL problems.
• Contributions to open-source projects in RL, LLMs, or ML infrastructure.
• Familiarity with data lakes and warehouses (Snowflake, BigQuery, Redshift).
Company:
Autonomous AI to help build and maintain data pipelines using your infrastructure. Founded in 2024, the company is headquartered in San Francisco, USA, with a team of 2-10 employees. The company is currently Early Stage.

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