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Reinforcement Learning Internship Jobs (NOW HIRING)

Modal, Inc

ML Research Intern

New York, NY · On-site

We are looking for PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our ...

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How much do reinforcement learning internship jobs pay per hour?

As of Sep 2, 2026, the average hourly pay for reinforcement learning internship 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 a reinforcement learning internship?

A Reinforcement Learning Internship is a temporary position, often for students or recent graduates, where you work on projects involving reinforcement learning—a type of machine learning where agents learn by interacting with their environment to achieve goals. Interns typically assist with research, data analysis, algorithm development, and experimentation under the supervision of experienced professionals. This role provides hands-on experience with RL frameworks, coding in languages like Python, and exposure to real-world applications such as robotics, gaming, or autonomous systems. The internship helps build practical skills and can pave the way for advanced study or a career in artificial intelligence research.

What are some common challenges faced during a reinforcement learning internship and how can I prepare for them?

As a Reinforcement Learning Intern, you may encounter challenges such as tuning hyperparameters, managing computational resources, and understanding the intricacies of reward design. Interns often work with large datasets and complex environments, which can be resource-intensive and require efficient coding skills. To prepare, it's helpful to familiarize yourself with popular RL frameworks (like TensorFlow or PyTorch), brush up on mathematical concepts such as Markov Decision Processes, and practice implementing algorithms from academic papers. Collaboration with senior researchers and regular code reviews are also key aspects of the internship experience.

What are the key skills and qualifications needed to thrive as a reinforcement learning intern, and why are they important?

To thrive as a Reinforcement Learning Intern, you need a strong background in mathematics (especially probability, statistics, and linear algebra), programming proficiency (commonly in Python), and foundational knowledge of machine learning concepts. Experience with libraries and frameworks such as TensorFlow, PyTorch, OpenAI Gym, and familiarity with relevant research papers or coursework are highly beneficial. Analytical thinking, creativity, and effective communication skills help interns solve complex problems and collaborate with research teams. These skills are crucial for contributing to innovative RL projects and efficiently learning from real-world experimentation.

What is the difference between Reinforcement Learning Internship vs Machine Learning Internship?

AspectReinforcement Learning InternshipMachine Learning Internship
Required SkillsReinforcement learning algorithms, Python, data analysisSupervised/unsupervised learning, Python, data preprocessing
Work EnvironmentResearch labs, AI startups, tech companiesTech firms, research institutions, data-driven companies
Industry UsageSpecialized in decision-making models and sequential learningBroader applications including classification, regression, clustering

Reinforcement Learning Internship focuses on decision-making algorithms and sequential learning, often in research or AI startup environments. Machine Learning Internship covers a wider range of algorithms and applications, suitable for various industries. Both roles require programming skills and a background in data science, but reinforcement learning internships are more specialized in AI decision systems.

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Reinforcement Learning Internship jobs near you
Infographic showing various Reinforcement Learning Internship job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $35,436 per year, or $17 per hour.

Internship - 2024 Summer Intern, PhD Research Scientist, Generative AI

Waymo

Mountain View, CA • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Waymo is an autonomous driving technology company with a mission to make it safe and easy for people and things to get where they're going. Since our start as the Google Self-Driving Car Project in 2009, Waymo has been focused on building the Waymo Driver-The World's Most Experienced Driver-to improve everyone's access to mobility while saving thousands of lives now lost to traffic crashes. Our Waymo Driver powers Waymo One, our fully autonomous ride-hailing service, as well as Waymo Via, our trucking and local delivery service. To date, Waymo has driven over 20 million miles autonomously on public roads across 25 U.S. cities and conducted over 20 billion miles of simulation testing.
At Waymo, we are mission-driven and believe deeply in the opportunity of autonomous driving technology to improve mobility and make people's lives better. We are united by purpose and responsibility (for our employees and riders alike). We are looking for kind, committed, employees who have integrity, dream big, work together as one team and create a sense of belonging for one another that is the foundation of our culture. We want each team member to feel welcomed and included in every step of our exciting journey.

The mission of the Waymo Research team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. Research areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.

Waymo interns work alongside leaders in the industry on projects that deliver significant impact to the company. We believe learning is a two-way street: leveraging your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship!

In this role, you'll:

  • Work on open-ended ML research problems for realistic simulation for the autonomous vehicle's driving environment.
  • Frame the open-ended real-world problems into well-defined ML problems; develop and apply cutting-edge ML approaches (deep learning, generative modeling, reinforcement learning, etc) to these problems; scale them to Google-sized data pipelines; and streamline them to run in real-time on the cars.
  • Collaborate with other teams, including the ML infrastructure, simulation and systems engineering teams, as well as Google Brain, DeepMind and academia.
  • Get an opportunity to publish findings in academic conferences and journals, and/or externally publicize the work in public-facing blog posts.

At a minimum we'd like you to have:

  • Currently enrolled in a PhD program in Computer Science, Robotics, similar technical field of study
  • Experience solving problems using Machine Learning with Tensorflow, JAX or equivalent tools
  • Ability to collaborate within and across teams
  • Ability to independently drive a reasonably-scoped, open-ended research project
  • Strong experience programming in Python with robust and efficient code

It's preferred if you have:

  • Strong track record of high quality ML research, for example demonstrated by conference publications in venues such as CVPR, ICCV, ECCV, ICML, NeurIPS, ICLR, etc.
  • Experience applying machine learning to sequence modeling, behavior forecasting, simulation, or robotics
  • Experience with generative models (e.g., autoregressive models, diffusion models, GANs, VAEs, etc.)

Apply