Lead Research Engineer - Applied AI
Bay Area, California
AI /
Full Time /
Hybrid
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.
