Lead the development of autonomous AI systems using reinforcement learning and LLMs, focusing on customer interaction optimization and collaboration with engineering and product teams.
About Level AILevel 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.
- BonusExperience with dialog agents, retrieval-augmented generation (RAG), or multi-agent collaboration frameworks.
- Background in building or scaling tool-using agents in enterprise contexts.
Why Join Us? Work at the frontier of LLM + agentic system research applied to real business problems.
Collaborate with a world-class team in a high-growth, Series C startup.
Competitive salary, equity, and benefits.
Top Skills
Hugging Face
Large Language Models
PyTorch
Ray Rllib
Reinforcement Learning
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