Lead Applied AI Researcher to spearhead advancements in agentic AI systems, building intelligent, decision-making agents powered by reinforcement learning, LLM fine-tuning, and multi-agent frameworks to enhance the AI-native CX platform.
Responsibilities
Design and build agentic systems for multi-step tasks
Apply and adapt reinforcement learning techniques
Fine-tune and optimize large language models for dialog management
Lead rapid prototyping and applied research on intelligent agent behavior
Collaborate with engineering, product, and data teams
Stay current with advancements in open-source LLMs and RL frameworks
Publish internal whitepapers and influence long-term AI strategy
Requirements
Experience in CS, Machine Learning, or a related field
5-10+ years in applied AI roles
Expertise in Reinforcement Learning, Agentic systems, and LLM fine-tuning
Proficiency with PyTorch, Hugging Face, Ray RLlib, or similar libraries
Experience shipping research to production
Strong publication record or open-source contributions a plus
Experience with dialog agents, retrieval-augmented generation, or multi-agent collaboration frameworks a bonus
Background in building or scaling tool-using agents in enterprise contexts a bonus