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Reinforcement Learning Intern Jobs in Berkeley, CA

Workato

Intern, AI Engineering

San Francisco, CA

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 ...

Workato

Intern, AI Engineering

San Francisco, CA · On-site

$19.75 - $25.50/hr

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 ...

Workato

Intern, AI Engineering

San Francisco, CA · On-site

$19.75 - $25.50/hr

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 ...

pony.ai

Research Intern - Deep Learning

Fremont, CA · On-site

$7.0K - $10K/mo

Frame the open-ended real-world problems into well-defined ML problems; develop and apply cutting-edge ML approaches (deep learning, reinforcement learning, imitation learning, etc) to these problems ...

pony.ai

Research Intern - Deep Learning

Fremont, CA · On-site

$7.0K - $10K/mo

Frame the open-ended real-world problems into well-defined ML problems; develop and apply cutting-edge ML approaches (deep learning, reinforcement learning, imitation learning, etc) to these problems ...

pony.ai

Research Intern - Deep Learning

Fremont, CA · On-site

$7.0K - $10K/mo

Frame the open-ended real-world problems into well-defined ML problems; develop and apply cutting-edge ML approaches (deep learning, reinforcement learning, imitation learning, etc) to these problems ...

Polymath AI Labs

AI Research Resident

San Francisco, CA · On-site

About Polymath Polymathisan applied research lab focused on advancing long-horizon agent capabilities through reinforcement learning. We design and scale simulation environments where agents learn to ...

Mechanize

Software Engineering Intern

Alameda, CA

$100/hr

About Mechanize Mechanize builds reinforcement learning environments that frontier AI labs use to train and evaluate their coding models. Learn more at mechanize.work. Why the work matters AI models ...

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How much do reinforcement learning intern jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for reinforcement learning intern in Berkeley, CA is $20.86, according to ZipRecruiter salary data. Most workers in this role earn between $17.64 and $23.56 per hour, depending on experience, location, and employer.

What is a reinforcement learning intern?

A Reinforcement Learning (RL) Intern is responsible for researching, developing, and testing RL algorithms to solve complex problems. They typically work on tasks such as implementing reinforcement learning models, optimizing reward functions, and running experiments in simulated environments. Interns collaborate with researchers and engineers to refine models and improve the efficiency of RL systems. They usually have experience in machine learning, deep learning, and programming languages like Python. The role provides hands-on experience in applying RL techniques to real-world applications.

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

To thrive as a Reinforcement Learning Intern, you need strong knowledge of machine learning fundamentals, programming proficiency (usually in Python), and a background in mathematics or computer science, often demonstrated through academic coursework or relevant projects. Familiarity with popular machine learning libraries such as TensorFlow, PyTorch, and RL-specific frameworks like OpenAI Gym is typically expected. Effective problem-solving skills, attention to detail, and the ability to communicate technical findings clearly are valuable soft skills in this position. These capabilities enable interns to contribute meaningfully to research and development efforts, bridging theory and practical application in real-world reinforcement learning projects.

What kinds of projects or tasks can I expect to work on as a reinforcement learning intern?

As a Reinforcement Learning Intern, you will typically work on tasks such as designing, implementing, and testing reinforcement learning algorithms, analyzing experimental results, and assisting with data preprocessing or environment development. You may also collaborate with senior researchers and engineers, participate in code reviews, and contribute to technical discussions or team meetings. In many organizations, interns are given the chance to work on real-world problems—ranging from optimizing robotic control systems to enhancing recommendation engines. This hands-on experience not only builds your technical expertise but also helps you develop valuable teamwork and communication skills, preparing you for a future career in AI or machine learning.

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Reinforcement Learning Intern jobs near you

Intern, AI Engineering

Workato

San Francisco, CA

Internship

Re-posted 11 hours ago

Job description

Workato AI LabAbout Workato AI Lab

Workato AI Lab is at the forefront of enterprise AI innovation, developing cutting-edge agentic systems that transform how businesses automate and optimize their workflows. Our team bridges academic research with real-world applications, creating AI systems that serve millions of users across global enterprises.

Responsibilities

We are seeking exceptional graduate students to join our AI Lab as Research Interns in San Francisco. You'll work on fundamental problems in LLM-based agentic systems and efficient AI infrastructure, with opportunities to publish your research while making direct impact on production systems serving enterprise customers.
We are now filling intern positions for Winter 2026 and Spring 2027. 

Research Areas

  • LLM Agent Systems: Design and implement intelligent agent architectures for complex enterprise automation tasks, including multi-agent collaboration, MCP, and reasoning frameworks

  • Efficient LLM Fine-tuning: Develop novel methods for parameter-efficient adaptation, alignment, and reinforcement learning for large language models

  • High-Performance LLM Inference: Optimize inference pipelines through systems-level innovations, kernel development, and deployment strategies

In this role, you will also be responsible to:

  • Conduct original research on LLM agent architectures and optimization techniques

  • Develop and evaluate novel algorithms with both academic rigor and production feasibility

  • Present your work at internal research seminars and external conferences

  • Mentor and collaborate with LLM  engineers on implementation and deployment

RequirementsQualifications / Experience / Technical Skills

  • Currently pursuing MS/PhD in Computer Science, Machine Learning, Natural Language Processing, or related fields

  • Publications at top-tier venues (ICML, NeurIPS, ICLR, ACL, EMNLP, NAACL)

  • Strong programming skills in Python and PyTorch

  • Ability to work in-person at our San Francisco office

  • Ability to work independently and collaborate across research and engineering teams

Preferred:

  • Experience with self-evolving agent systems

  • Proficiency in CUDA programming and custom kernel development for LLM operations

  • Background in reinforcement learning-based LLM fine-tuning 

  • Track record of contributions to production inference systems such as vLLM, TensorRT-LLM, SGLang, or Hugging Face ecosystem

  • Experience bridging academic research with production systems

  • Open-source contributions to widely-used ML infrastructure projects

(REQ ID: 2690)

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