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Reinforcement Learning Intern Jobs in California

Faire

Data Science Intern

San Francisco, CA

$75/hr

Apply exploration-exploitation strategies - including contextual bandits and reinforcement learning - to optimize recommendations under uncertainty * Advance recommendation quality through ...

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 ...

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Reinforcement Learning Intern information

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$8

$16

$23

How much do reinforcement learning intern jobs pay per hour?

As of Aug 8, 2026, the average hourly pay for reinforcement learning intern in California is $16.81, according to ZipRecruiter salary data. Most workers in this role earn between $14.23 and $18.99 per hour, depending on experience, location, and employer.

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.

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.

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Reinforcement Learning Intern jobs near you
Infographic showing various Reinforcement Learning Intern job openings in California as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $34,972 per year, or $16.8 per hour.

Data Science Intern

Faire

San Francisco, CA

$75/hr

Internship

Re-posted 25 days ago

Job description

Data Science Internship - Fall 2026

Faire leverages machine learning and data insights to transform the wholesale industry, giving independent retailers the tools to compete with large-scale e-commerce platforms and big-box stores. Our Data Science team builds and maintains the algorithmic systems - spanning search, personalization, recommendation, and ranking - that power our marketplace and help our customers thrive.

We are hiring Data Science interns across several teams and are looking for intellectually curious, self-directed problem solvers eager to work end-to-end on high-impact challenges, from data exploration to production-ready solutions.

Our internships are paid, 12-14 weeks in duration, with flexible start dates. Extensions are considered based on project scope and mutual interest.

Open Team

Search & Recommendation

  • Design and deploy state-of-the-art recommender systems that power ranking and discovery across the marketplace
  • Develop rich user and item representations through embeddings, sequence models, and graph-based methods
  • Build real-time and streaming data pipelines that enable dynamic, context-aware personalization at scale
  • Apply exploration-exploitation strategies - including contextual bandits and reinforcement learning - to optimize recommendations under uncertainty
  • Advance recommendation quality through improvements to diversification, novelty, and long-term user engagement
  • Own the full ML lifecycle: from problem formulation and modeling through offline evaluation and online experimentation

What You'll Do

  • Design, develop, and A/B test cutting-edge machine learning algorithms and analytical solutions, with guidance from senior technical leads
  • Communicate project objectives, methodologies, and results clearly to both immediate teammates and broader cross-functional stakeholders
  • Navigate the complexity of a two-sided marketplace, identifying and addressing the unique challenges that arise at the intersection of retailer and brand needs

What We're Looking For

All candidates must be currently enrolled or recently graduated Master's or PhD students in Computer Science, Operations Research, Statistics, Econometrics, or a related technical discipline. Beyond that, we're looking for team-specific experience:

Search & Recommendation Systems

  • Publications or submissions to top-tier venues such as KDD, RecSys, ICML, NeurIPS, WWW, or SIGIR
  • Experience with recommender systems (collaborative filtering, deep recommenders, ranking), representation learning and embeddings, sequential models (RNNs, Transformers for user behavior modeling), bandit and reinforcement learning methods, and large-scale retrieval and ranking systems
  • Familiarity with offline evaluation metrics (NDCG, MAP, recall) and online experimentation
  • Experience working with large-scale or production datasets

Pay rate:

San Francisco: the pay rate for this role is $75 USD per hour.

Actual hourly pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The pay range provided is subject to change and may be modified in the future.

Faire uses Artificial Intelligence (AI) to screen and select applicants for this position.

This job posting is for an existing vacancy.

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