Research Intern – Reinforcement Learning (RL)
Level AI · Mountain View, CA
- On site
- Internship
- $45,000 / year
- Mountain View, CA
Job highlights
- Design RL environments for customer interactions.
- Develop RL agents using real-world data.
- Define reward models and feedback loops.
- Enable learning from production datasets.
- Collaborate on deploying AI systems.
About the role
Research Intern – Reinforcement Learning (RL)
🚀 Build the next generation of Agentic AI with us
Our platform combines conversation intelligence, multimodal understanding, and agentic AI systems to power both human agents and autonomous AI agents across the entire customer experience lifecycle. A core part of this vision is our investment in custom Small Language Models (SLMs)—purpose-built for CX workflows—paired with reinforcement learning systems that continuously improve decision-making in real-world environments. We’re looking for a Research Intern (Reinforcement Learning) to join us in shaping this future.
What you’ll do:
- Design and build reinforcement learning environments that model real-world customer interaction workflows.
- Design RL agents that learn from these environments using real-world interaction data, rewards, and feedback loops.
- Define reward models and feedback loops using real-world signals (outcomes and human feedback).
- Enable learning from production data by structuring interaction traces into training-ready datasets for offline and online learning.
- Experiment with multi-agent systems and simulation frameworks for complex coordination and decision-making.
- Collaborate with engineering and product teams to deploy, evaluate, and iterate on learning systems in production at scale.
What we’re looking for:
- Currently pursuing (or recently completed) a degree in Computer Science, AI, Machine Learning, or related field.
- Strong understanding of reinforcement learning fundamentals.
- Familiarity with RL environments and training libraries such as Verl and Tinker.
- Strong foundation in probability, math, and optimization.
- Passion for building real-world AI systems.
Nice to have:
- Experience with RLHF, LLM/SLM fine-tuning, or model alignment.
- Exposure to agent-based systems or multi-agent RL.
- Prior research, projects, or publications in RL or applied ML.
- Experience working with large-scale or production datasets.
Why Level AI:
- Work on production-grade Agentic AI systems used by leading enterprises.
- Build alongside a team with deep expertise from Amazon, Google, and Meta.
- Be part of a fast-growing Series C AI company.
- Direct exposure to 0→1 AI innovation in CX and decisioning systems.
Key skills/competency:
- Reinforcement Learning
- Agentic AI
- Small Language Models (SLMs)
- Conversation Intelligence
- Machine Learning
- Python
- Data Science
- Algorithm Design
- Optimization
- Customer Experience (CX)
Skills & topics
- Research Intern
- Reinforcement Learning
- Machine Learning
- AI
- Agentic AI
- SLM
- Python
- Optimization
- Data Science
- Customer Experience
How to get hired
- Tailor your resume: Highlight RL, ML, and Python skills.
- Showcase projects: Emphasize RL environments and agent development.
- Research Level AI: Understand their agentic AI and SLM focus.
- Prepare for technicals: Practice RL concepts and probability questions.
- Articulate passion: Show your enthusiasm for real-world AI systems.
Technical preparation
Master RL fundamentals and algorithms.,Practice designing RL environments.,Brush up on probability and optimization.,Familiarize with Verl and Tinker libraries.
Behavioral questions
Explain a complex RL project.,Describe your passion for AI.,How do you handle research challenges?,Discuss teamwork and collaboration.
Frequently asked questions
- What specific reinforcement learning libraries are preferred for the Research Intern role at Level AI?
- The job description specifically mentions familiarity with RL environments and training libraries such as Verl and Tinker. While these are highlighted, a strong foundational understanding of reinforcement learning principles is paramount, and experience with other common RL libraries may also be considered.
- What kind of real-world data will I work with as a Research Intern at Level AI?
- As a Research Intern focusing on Reinforcement Learning at Level AI, you will work with real-world customer interaction data. This includes interaction traces, outcomes, and human feedback, which will be structured into training-ready datasets for both offline and online learning.
- Does Level AI encourage publication of research from interns?
- While not explicitly stated, Level AI is a fast-growing Series C AI company with a strong emphasis on research and innovation, particularly in agentic AI and SLMs. Prior research, projects, or publications in RL or applied ML are listed as 'nice to have,' suggesting they value such contributions. It's advisable to inquire about their policy on intern publications during the interview process.
- What is the typical interview process for a Research Intern at Level AI?
- The interview process likely involves an initial screening to assess your academic background and foundational knowledge in RL and ML. This is typically followed by technical interviews focusing on reinforcement learning fundamentals, probability, math, and optimization. You may also be asked about your passion for building real-world AI systems and potentially discuss past projects or research.
- What does 'Agentic AI' mean in the context of Level AI's work?
- In the context of Level AI, 'Agentic AI' refers to AI systems, particularly Small Language Models (SLMs) and reinforcement learning agents, that can autonomously make decisions and take actions within customer experience workflows. These agents are designed to improve customer interactions and outcomes across the entire lifecycle.