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Summer Reinforcement Learning Intern Jobs (NOW HIRING)

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PhD Research Intern

Palo Alto, CA · On-site

PhD Intern Where multiple locations are listed for this role, the position may be based in any of ... Reinforcement learning, Large language/vision-language models, Computer vision and multimodal ...

Workato

Intern, AI Engineering

San Francisco, CA · On-site

We are now filling intern positions for Winter 2026 and Spring 2027. Research Areas * LLM Agent ... Develop novel methods for parameter-efficient adaptation, alignment, and reinforcement learning for ...

Pravāh

ML Summer Intern

San Francisco, CA · On-site

$5K - $10K/mo

We apply modern machine learning to complex physical infrastructure problems spanning grid ... a summer internship. As an ML Intern at Pravah, you will work on real, open-ended technical ...

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Summer Reinforcement Learning Intern information

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

As of Jun 9, 2026, the average hourly pay for summer 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 types of projects or tasks can I expect as a Summer Reinforcement Learning Intern?

As a Summer Reinforcement Learning Intern, you can expect to work on projects ranging from implementing and testing RL algorithms to analyzing experiment results and optimizing model performance. Interns often collaborate with experienced researchers and engineers, contributing to both independent and team projects. You may also be involved in literature reviews, setting up simulation environments, and presenting findings to your team. The role provides hands-on experience with real-world RL applications, and you’ll have the opportunity to learn from feedback and mentorship throughout your internship.

What is the difference between Summer Reinforcement Learning Intern vs Summer Data Science Intern?

AspectSummer Reinforcement Learning InternSummer Data Science Intern
Required CredentialsUndergraduate or graduate in CS, AI, or related fields; some knowledge of machine learning and programmingUndergraduate or graduate in Data Science, Statistics, or related fields; strong analytical and programming skills
Work EnvironmentResearch-focused, experimental projects, often in AI and machine learning teamsData analysis, modeling, visualization, and reporting tasks across various departments
Employer & Industry UsageTech companies, AI startups, research labsTech firms, finance, healthcare, and consulting industries

The Summer Reinforcement Learning Intern role focuses on developing and testing reinforcement learning algorithms, often within AI research teams. In contrast, the Summer Data Science Intern role involves broader data analysis and modeling tasks. Both roles require programming skills and are common in tech industries, but they differ in their specific focus and project types.

What are Summer Reinforcement Learning Interns?

Summer Reinforcement Learning Interns are students or recent graduates who work temporarily, usually during the summer, to gain hands-on experience in reinforcement learning, a subfield of machine learning. Their responsibilities often include assisting with the development and testing of algorithms, analyzing data, and collaborating with research teams on projects related to artificial intelligence. This role provides an opportunity to apply theoretical knowledge from coursework to real-world problems, often resulting in valuable skills and networking opportunities for future careers in AI or data science.

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

To thrive as a Summer Reinforcement Learning Intern, you need a solid background in computer science, mathematics (particularly probability and linear algebra), and experience with machine learning frameworks. Familiarity with Python, TensorFlow or PyTorch, and a strong grasp of reinforcement learning algorithms are typically required, often supported by coursework or relevant certifications. Strong problem-solving skills, curiosity, and effective communication help you stand out in collaborative research and fast-paced project environments. These skills are crucial for contributing to innovative AI projects, rapidly learning new concepts, and effectively sharing findings with mentors and team members.
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Summer Reinforcement Learning Intern jobs near you
Robot Learning Engineering Intern

Robot Learning Engineering Intern

Agility Robotics

Pittsburgh, PA • On-site

$14.50 - $19.50/hr

Other

Posted 15 days ago

Job description

About the Role

We are looking for a graduate-level Robot Learning Engineering Intern to join our Skills team and support the development of learned manipulation behaviors for our humanoid robots. You will work at the intersection of robot learning, teleoperation, data collection, and on-robot evaluation, helping the team build and validate learning workflows that can support future deployment on humanoid systems.

This internship is especially well-suited for a graduate student who wants hands-on experience with a self-contained robot learning testbed and is excited to explore how high-quality demonstrations, sensing, and evaluation can be used to develop force-aware or contact-rich manipulation policies. You will work closely with engineers developing Learning from Demonstration (LfD) systems and learned robot skills, contributing to the tooling, experiments, and analysis needed to make these approaches useful in practice.

Key Responsibilities

  • Support demonstration data collection for learned robot behaviors using teleoperation and other operator-in-the-loop systems
  • Help build and improve a robot learning testbed, including integration of teleop interfaces, cameras, and other sensing required for data collection and evaluation
  • Assist with experiments focused on contact-rich or force-aware manipulation behaviors
  • Build and improve tooling for data ingestion, annotation, validation, replay, and analysis
  • Assist in evaluating learned policies in simulation and on real robot hardware
  • Partner with engineers to debug failures and improve system robustness
  • Analyze experiments and generate insights around data quality, policy behavior, and deployment readiness
  • Contribute to internal tools and infrastructure for robot learning development
  • Document findings and communicate results to mentors and cross-functional stakeholders

About You

  • Currently pursuing an MS or PhD in Robotics, Computer Science, Machine Learning, or a related field
  • Strong software engineering fundamentals and proficiency in Python
  • Background in robotics, machine learning, embodied AI, controls, or autonomous systems
  • Familiarity with at least some of the following: imitation learning, reinforcement learning, robot manipulation, force control, teleoperation, robot sensors, or object perception
  • Strong analytical, experimental, and debugging skills
  • Comfortable working hands-on with robotic systems in a lab environment

Bonus Qualifications

  • Experience with behavior cloning, Learning from Demonstration, offline RL, or learned control policies
  • Experience with robot data collection systems, VR/XR tools, haptics, or teleoperation interfaces
  • Experience with force/torque sensing or contact-rich manipulation tasks
  • Experience evaluating algorithms on real hardware rather than simulation alone

This is an onsite internship at our office in Pittsburgh, PA - minimum 4 days/week onsite.

Duration: Internship (3-6+ months, flexible)

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