Research Intern – Reinforcement Learning (RL) - Onsite
Level AI · San Francisco Bay Area
- On site
- Full-time
- $60,000 / year
- San Francisco Bay Area
Job highlights
- Design RL environments and agents for customer interactions.
- Develop reward models using real-world data and feedback.
- Structure data for offline and online learning systems.
- Collaborate on deploying AI systems at scale.
- Work on advanced Agentic AI with expert teams.
About the role
Research Intern – Reinforcement Learning (RL) - Onsite
🚀 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
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Key skills/competency
- Reinforcement Learning
- Agentic AI
- Small Language Models (SLMs)
- Customer Experience (CX)
- Machine Learning
- Python
- Deep Learning
- Data Science
- Research
- Algorithm Design
Skills & topics
- Reinforcement Learning
- Research Intern
- AI
- Machine Learning
- Agentic AI
- Customer Experience
- SLM
- Python
- RLHF
- Data Science
How to get hired
- Tailor your resume: Highlight your reinforcement learning projects, ML coursework, and any experience with LLMs or SLMs. Quantify achievements whenever possible.
- Showcase your passion: Emphasize your understanding of RL fundamentals and your eagerness to build real-world AI systems. Include personal projects or GitHub repositories.
- Prepare for technical interviews: Brush up on probability, math, optimization, and RL concepts. Be ready to discuss your experience with RL libraries like Verl and Tinker.
- Understand Level AI's mission: Research their platform, their focus on Agentic AI, and their use of SLMs and RL. Show how your skills align with their vision.
Technical preparation
Master RL fundamentals and algorithms.,Practice with RL libraries like Verl, Tinker.,Strengthen probability, math, and optimization.,Build personal projects with RL agents.
Behavioral questions
Describe a challenging AI project you faced.,How do you handle ambiguity in research?,Explain your passion for building real AI.,How would you collaborate with engineering teams?
Frequently asked questions
- What specific reinforcement learning libraries does Level AI use for their Research Intern role?
- The Level AI Research Intern role specifically mentions familiarity with RL environments and training libraries such as Verl and Tinker. It's advisable to highlight any experience you have with these or similar libraries.
- What kind of real-world data will I work with as a Research Intern at Level AI?
- As a Research Intern at Level AI, you will work with real-world customer interaction data, including outcomes and human feedback. This data will be used to define reward models and train RL agents.
- Is there an opportunity to work with LLM or SLM fine-tuning during the internship?
- Yes, experience with RLHF, LLM/SLM fine-tuning, or model alignment is listed as a 'nice to have' for the Research Intern role, suggesting it's an area of interest and potential involvement.
- What is the expected educational background for the Research Intern position at Level AI?
- Candidates for the Research Intern role are expected to be currently pursuing or have recently completed a degree in Computer Science, AI, Machine Learning, or a related field. A strong foundation in probability, math, and optimization is also required.
- How does Level AI use AI tools in its hiring process for the Research Intern role?
- Level AI may use AI tools to assist in reviewing applications, analyzing resumes, and assessing responses. However, they emphasize that these tools support, not replace, human judgment, and final hiring decisions are made by humans.
- What are the key responsibilities for a Research Intern in Reinforcement Learning at Level AI?
- Key responsibilities include designing RL environments and agents, defining reward models, structuring data for learning, experimenting with multi-agent systems, and collaborating on deploying AI systems in production.
- What makes working at Level AI unique for an AI Research Intern?
- Level AI offers the chance to work on production-grade Agentic AI systems, learn from experts from top tech companies, and be part of a fast-growing Series C AI company focused on 0→1 AI innovation in CX.