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Internship Deep Reinforcement Learning Jobs (NOW HIRING)

MLabs

Reinforcement Learning Engineer

New York, NY · On-site

$87K - $118K/yr

Reinforcement Learning (RL) Engineer Location: New York (Office) On-site Full-time Compensation ... A deep-dive assessment into RL architecture, simulation frameworks, and live production experience.

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 Jun 21, 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 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.

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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 June 2026, with employment types broken down into 1% Internship, 53% Full Time, 44% Part Time, 1% Temporary, and 1% Contract. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $35,436 per year, or $17 per hour.
Helix AI Engineer, Reinforcement Learning

Helix AI Engineer, Reinforcement Learning

Figure

San Jose, CA • On-site

Full-time

Posted 14 days ago

Job description

Figure is an AI robotics company developing autonomous general-purpose humanoid robots. Our goal is to build embodied AI systems that can perceive, reason, and act in the real world. Figure is headquartered in San Jose, CA, and this role requires 5 days/week in-office collaboration.
Our Helix team is responsible for developing the core AI systems that power humanoid autonomy. We are looking for a Helix AI Engineer, Reinforcement Learning to develop learning systems that enable robots to acquire skills through interaction, feedback, and experience.
This role focuses on applying and advancing reinforcement learning across simulation and real-world environments-improving policy performance, robustness, and long-horizon decision-making in embodied systems.
Responsibilities
  • Design and implement reinforcement learning algorithms for embodied agents operating in real-world and simulated environments
  • Train policies that learn from interaction, feedback, and large-scale experience across diverse tasks
  • Develop reward modeling, credit assignment, and exploration strategies for complex, long-horizon behaviors
  • Improve policy robustness to real-world challenges such as noise, partial observability, and environment variability
  • Work across online and offline RL settings, including learning from large-scale logged robot data
  • Collaborate closely with pretraining, video, generative, agent, and robot learning teams to integrate RL into the full autonomy stack
  • Build scalable training systems for RL, including distributed rollouts, simulation infrastructure, and experiment management
  • Design evaluation frameworks to measure policy performance, stability, and generalization
Requirements
  • Experience developing and applying reinforcement learning algorithms in complex environments
  • Strong understanding of RL fundamentals (e.g., policy optimization, value methods, model-based RL)
  • Experience training policies in simulation and/or real-world systems
  • Proficiency in Python and deep learning frameworks such as PyTorch
  • Experience with large-scale experimentation and distributed training systems
  • Strong experimental rigor and ability to diagnose and improve learning systems
  • Solid software engineering skills and ability to build scalable, reliable systems
  • Ability to operate independently and drive ambiguous, high-impact technical problems
Bonus Qualifications
  • Experience applying RL to robotics, control systems, or embodied AI
  • Experience with large-scale RL infrastructure (distributed rollouts, simulation at scale)
  • Background in offline RL, imitation learning, or hybrid learning approaches
  • Experience with reward modeling or human-in-the-loop learning
  • Experience at leading AI labs such as OpenAI, Google DeepMind, Anthropic, or xAI
  • Familiarity with robotics systems, simulation environments, or real-world deployment constraints
  • Publication record in reinforcement learning, machine learning, or robotics

The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.

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