Principal Applied Scientist

As a Principal Applied Scientist, you will lead the architecture, research, and productization of these next-generation ML systems, bridging deep research with deployment at scale and shaping the future of enterprise automation.

Responsibilities

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.

Requirements

10+ years of industry experience in machine learning, including substantial experience building and operating ML systems in production; Deep expertise in modern ML frameworks and libraries, strong proficiency in Python, and experience integrating ML models into large-scale software systems; Proven experience with large language models or foundation models, including fine-tuning, data preparation, inference optimization, or model evaluation; Experience with large datasets, distributed training or inference, scalable architecture design, and optimization for latency, throughput, and cost; Excellent communication skills with the ability to translate complex ML concepts to product and business audiences; Demonstrated technical leadership in mentoring, influencing strategy, balancing research with delivery, and driving high-impact outcomes.

Nice to have

Advanced degree (MS or PhD preferred) in Computer Science, Machine Learning, AI, or a related field, or equivalent experience.

Benefits

Flexibility in when and where work gets done