About the job
Join UiPath: Pioneers in Automation
At UiPath, we harness the transformative potential of automation to redefine the way the world operates. Our commitment lies in developing industry-leading enterprise software that unleashes this potential.
We seek individuals who are inquisitive, driven, collaborative, and authentic, those who thrive in a dynamic, rapidly evolving environment and are passionate about our mission.
Are you one of them?
As a Principal Applied Scientist, you will spearhead the design, research, and implementation of cutting-edge Machine Learning systems, seamlessly integrating deep research with large-scale deployment to shape the future of enterprise automation.
Key Responsibilities
• Develop and drive the technical vision for agent-based automation, focusing on how autonomous agents utilize LLMs, reinforcement learning, simulation environments, tool use, and multi-step reasoning to enhance the UiPath platform.
• Design, prototype, and implement advanced ML and AI systems, including LLM fine-tuning, multimodal pipelines, computer-use modeling, agent orchestration frameworks, and decision-making systems.
• Oversee the development and execution of ML infrastructure and services for model training, fine-tuning, large-scale inference, model serving, monitoring, drift detection, continuous learning loops, and ML operations for agentic systems.
• Collaborate extensively with product, engineering, design, and go-to-market teams to translate innovative research into customer-facing solutions.
• Investigate leading-edge techniques in prompting, retrieval-augmented generation, chain-of-thought, tool use, long-term memory, and RL or imitation learning for agent behavior, applying them to automation workflows.
• Establish best practices, frameworks, and metrics for evaluating agentic systems, encompassing offline evaluation, simulation environments, human-in-the-loop feedback, A/B testing, and analyses of cost, latency, and quality.
• Act as a technical leader and mentor across ML engineering, data science, and software engineering, promoting a culture of experimentation, reproducibility, version control, and thorough evaluation.
• Represent UiPath in the broader community through publications, open-source contributions, conference participation, and collaboration with academic and ecosystem partners.
