Research Intern – Reinforcement Learning (RL)… at Level AI · Bay Area

Summary: Level AI is focused on building the next generation of Agentic AI systems that enhance customer experience. They are seeking a Research Intern in Reinforcement Learning to design and build environments, develop RL agents, and collaborate with teams to improve AI systems. 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 Required Qualifications: - 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 Preferred Qualifications: - 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 Required Skills: Reinforcement Learning, Probability, Machine Learning Important Skills: RL Environments, Multi-agent Systems Nice-to-Have Skills: Passion for AI, Research Experience, Collaboration Skills