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Remote Reinforcement Learning Intern Jobs (NOW HIRING)

Statheros

Develop and train reinforcement learning models for real-world applications, focusing on efficiency ... Remote work location. * Competitive salary. * Flexible work schedule. * Opportunities for ...

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Remote Reinforcement Learning Intern information

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How much do remote reinforcement learning intern jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for remote reinforcement learning intern 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 does a Remote Reinforcement Learning Intern do?

A Remote Reinforcement Learning Intern assists with research and development projects that focus on reinforcement learning, a type of machine learning where agents learn to make decisions by trial and error. Their tasks often include implementing algorithms, running experiments, analyzing results, and contributing to academic papers or practical applications. Working remotely, they collaborate with teams using online tools and communicate progress regularly. The role is ideal for students or recent graduates who want to gain hands-on experience in artificial intelligence and machine learning.

What are some common challenges faced by remote reinforcement learning interns, and how can they be overcome?

Remote reinforcement learning interns often encounter challenges related to communication and collaboration, especially when working with distributed teams. It can also be difficult to access computational resources or receive timely feedback on experiments. To overcome these challenges, it's important to proactively schedule regular check-ins with mentors, utilize collaborative tools (such as Slack or GitHub), and ensure a reliable internet connection. Additionally, keeping detailed documentation and being transparent about progress can help facilitate smoother teamwork and problem-solving.

What are the key skills and qualifications needed to thrive as a Remote Reinforcement Learning Intern, and why are they important?

To thrive as a Remote Reinforcement Learning Intern, you need a strong background in mathematics, programming (especially Python), and foundational knowledge of machine learning concepts, typically demonstrated through coursework or relevant projects. Familiarity with reinforcement learning libraries (such as TensorFlow, PyTorch, or OpenAI Gym), version control systems like Git, and possibly cloud computing platforms is highly valuable. Excellent problem-solving abilities, self-motivation, and effective remote communication skills help interns excel in independent and collaborative tasks. These skills are essential for contributing to innovative research and development projects while working efficiently in a distributed team environment.
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Remote Reinforcement Learning Intern jobs near you

Artificial Intelligence (AI) Engineer / Developer (Remote)

Statheros

On-site, Remote

Contractor

Posted 11 days ago

Job description

About Us
Statheros is a small DEFTECH firm focused on developing cutting-edge AI and autonomy systems for the US Department of Defense. Our team is passionate about building intelligent systems that solve complex problems. We are looking for a talented AI Engineer specializing in Proximal Policy Optimization (PPO) to lead the development of AI-enabled algorithms that automate the operation of air traffic radar systems.
Job Responsibilities
  • Design, implement, and optimize Proximal Policy Optimization (PPO) algorithms for domain-specific use cases.
  • Develop and train reinforcement learning models for real-world applications, focusing on efficiency and scalability.
  • Collaborate with cross-functional teams to integrate PPO models into production systems.
  • Analyze model performance and experiment with hyperparameter tuning to achieve optimal results.
  • Stay up-to-date with the latest research and advancements in reinforcement learning and apply them to enhance existing solutions.
  • Build robust pipelines for training, evaluation, and deployment of RL models.
  • Document workflows, methodologies, and code for reproducibility and knowledge sharing.
Qualifications
  • Educational Background: Bachelor's or Master's degree in Computer Science, Machine Learning, AI, Mathematics, or related fields. Ph.D. is a plus.
  • Experience:
    • 4+ years of professional experience in machine learning, with a focus on reinforcement learning.
    • Demonstrated expertise in implementing and optimizing PPO or similar reinforcement learning algorithms.
    • Hands-on experience with frameworks like TensorFlow, PyTorch, or JAX.
  • Technical Skills:
    • Strong programming skills in Python; familiarity with Rust or other languages is a plus.
    • Proficiency in designing and running RL experiments in simulated or real-world environments.
    • Experience with distributed training systems for reinforcement learning.
    • Solid understanding of policy gradient methods and reinforcement learning theory.
  • Soft Skills:
    • Excellent problem-solving skills and the ability to work in a collaborative, fast-paced environment.
    • Strong communication skills for presenting findings and collaborating with interdisciplinary teams.
Preferred Qualifications
  • Experience in applying PPO to [specific domain, e.g., robotics, gaming, finance, etc.]
  • Familiarity with OpenAI Gym, RLlib, or other RL development environments
  • Knowledge of parallel computing and GPU acceleration for large-scale RL tasks
What We Offer
  • Remote work location.
  • Competitive salary.
  • Flexible work schedule.
  • Opportunities for professional development and research contributions
  • Access to state-of-the-art resources and tools for AI development.
  • The chance to work on groundbreaking projects with a talented and passionate team.

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