Research Intern – Reinforcement Learning (RL)

About the Role

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.

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.

What You'll Need

• 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, maths, and optimization.
• Passion for building real-world AI systems.

Resume Tip

Highlight experience with RLHF, LLM/SLM fine-tuning, or model alignment, as well as exposure to agent-based systems or multi-agent RL.