What You Will Do
• Define and drive technical strategy for agent-based automation, including how autonomous agents use LLMs, reinforcement learning, simulation environments, tool use, and multi-step reasoning to integrate with the UiPath platform.
• Architect, prototype, and deploy advanced ML and AI systems, covering LLM fine-tuning, multimodal pipelines, computer-use modeling, agent orchestration frameworks, and decision-making systems.
• Lead the design and implementation 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.
• Partner closely with product, engineering, design, and go-to-market teams to translate research advances into customer-facing capabilities.
• Research state-of-the-art techniques in prompting, retrieval-augmented generation, chain-of-thought, tool use, long-term memory, and RL or imitation learning for agent behavior, and apply them to automation workflows.
• Establish best practices, frameworks, and metrics for evaluating agentic systems, including offline evaluation, simulation environments, human-in-the-loop feedback, A/B testing, and cost, latency, and quality analysis.
• Serve as a technical leader and mentor across ML engineering, data science, and software engineering, fostering a culture of experimentation, reproducibility, versioning, and rigorous evaluation.
• Represent UiPath in the broader community through publications, open-source contributions, conference participation, and collaboration with academia or ecosystem partners.