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.

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.

What We Are Looking For
• Advanced degree (MS or PhD preferred) in Computer Science, Machine Learning, AI, or a related field, or equivalent experience.
• 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.

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Maybe you don't tick all the boxes above-but still think you'd be great for the job? Go ahead, apply anyway. Please. Because we know that experience comes in all shapes and sizes-and passion can't be learned.

Many of our roles allow for flexibility in when and where work gets done. Depending on the needs of the business and the role, the number of hybrid, office-based, and remote workers will vary from team to team. Applications are assessed on a rolling basis and there is no fixed deadline for this requisition. The application window may change depending on the volume of applications received or may close immediately if a qualified candidate is selected.