Inactive
L

Level AI

Optimizes call center data with AI

Research Intern/Co-op - Reinforcement Learning

AI ResearchPosted on Mar 28
Internship · Summer 2026California, USANo salary listedIn-person

About the Job

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

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

Desired 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

About the Company

Level AI

Optimizes call center data with AI

Level AI provides software that improves customer experience and operational efficiency in the BPO sector by integrating human and machine intelligence. Its platform unifies call center data from multiple sources and uses intelligent automation and machine learning to automate tasks, guide decisions, and improve interactions, all delivered through a subscription model. It differentiates itself by offering end-to-end data integration across silos and blending human expertise with ML to boost sales conversions and QA productivity in call centers. The goal is to help BPO providers deliver better customer experiences while making operations more efficient and cost-effective.

Company size
201-500
Company stage
Series C
Total funding
$74.4M
Headquarters
Mountain View, CA
Founded
2018

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