Research Intern – Reinforcement Learning (RL) - Onsite

Level AI

19h•Onsite

About The Position

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)âpurpose-built for CX workflowsâpaired with reinforcement learning systems that continuously improve decision-making in real-world environments. Weâre looking for a Research Intern (Reinforcement Learning) to join us in shaping this future.

Requirements

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 Haves

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

Responsibilities

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.