Principal Applied Scientist
Core
Lead the architecture, research, and productization of next-generation ML systems for agent-based automation, bridging deep research with deployment at scale.
Role type
Principal Applied Scientist (Agentic Systems & LLMs)
Builds
Autonomous agents using LLMs, reinforcement learning, simulation environments, tool use, and multi-step reasoning integrated with the UiPath platform.
Domain
Enterprise Automation / Artificial Intelligence / Large Language Models
Deliverable
production ML models
Required skills
LLM fine-tuning, reinforcement learning, agent orchestration, distributed training, scalable architecture design, Python, model evaluation, simulation environments
Preferred skills
Foundation models, multimodal pipelines, computer-use modeling, long-term memory, imitation learning, open-source contributions
Technologies
Python, LLMs, RL, simulation environments, tool use frameworks
Responsibilities
Define technical strategy for agent-based automation; Architect and deploy advanced ML/AI systems; Lead ML infrastructure design for training and inference; Partner with product and engineering teams; Research state-of-the-art techniques for agentic behavior; Establish evaluation frameworks and metrics; Mentor ML engineering and data science teams.
Seniority
Principal, strategy & mentorship
