About Level AI

Level AI is revolutionizing customer experience by transforming contact centers into strategic assets through state-of-the-art AI. Our platform leverages LLMs and real-time intelligence to understand complex customer interactions and drive better business outcomes.

Role Overview

We’re looking for a hands-on and visionary Lead Applied AI Researcher to spearhead cutting-edge advancements in agentic AI systems. This role will focus on building intelligent, decision-making agents powered by reinforcement learning, LLM fine-tuning, and multi-agent frameworks to enhance our AI-native CX platform.

Key Responsibilities

• Design and build agentic systems that operate autonomously across multi-step tasks.
• Apply and adapt reinforcement learning (RL) techniques to real-world interaction and decision-making problems.
• Fine-tune and optimize large language models (LLMs) for dialog management, summarization, and real-time analysis of customer interactions.
• Lead rapid prototyping and applied research on intelligent agent behavior, planning, and memory across various domains.
• Collaborate closely with engineering, product, and data teams to bring research into production at scale.
• Stay current with advancements in open-source LLMs, RL frameworks, and cognitive architectures, integrating them when relevant.
• Publish internal whitepapers and influence long-term AI strategy at Level AI.

Qualifications

• Experience in CS, Machine Learning, or a related field.
• 5 - 10+ years in applied AI roles with proven contributions to LLM, RL, or agentic research.
• Experience expertise in:
• Reinforcement Learning (e.g., PPO, GRPO)
• Agentic systems (planning, memory, autonomy)
• LLM fine-tuning (PEFT, LoRA, RLHF)
• Proficiency with PyTorch, Hugging Face, Ray RLlib, or similar libraries.
• Experience shipping research to production in high-stakes environments.
• Strong publication record or open-source contributions a plus.

Bonus

• Experience with dialog agents, retrieval-augmented generation (RAG), or multi-agent collaboration frameworks.
• Background in building or scaling tool-using agents in enterprise contexts.