Transform enterprise operations with the UiPath Platform™ for business orchestration and automation, enabling AI agents, robots, and people to deliver secure, compliant outcomes at scale. Backed by Accel, Sequoia and IVP.
About the role
Lead research and development of large-scale foundation models and agentic AI systems for enterprise automation. Design novel approaches for post-training, model alignment, reinforcement learning, preference optimization, and synthetic data generation.
What they're looking for
- We're looking for scientists who have successfully taken frontier AI research from idea to production
- Foundation model training
- LLM post-training
- Reinforcement Learning from Human Feedback (RLHF)
- Preference optimization (DPO, PPO, GRPO or similar methods)
- Reward modeling
More about this role
The people at UiPath believe in the transformative power of automation to change how the world works. We’re committed to creating category-leading enterprise software that unleashes that power.
To make that happen, we need people who are curious, self-propelled, generous, and genuine. People who love being part of a fast-moving, fast-thinking growth company. And people who care—about each other, about UiPath, and about our larger purpose.
Could that be you?
At UiPath, we believe the next generation of software won't simply automate tasks—it will reason, plan, adapt, and act autonomously.
As a Principal Applied Scientist, you'll help invent that future.
You will lead the research and development of frontier AI systems that combine foundation models, multimodal reasoning, agentic workflows, and post-training techniques to build autonomous software capable of operating enterprise applications like a human.
This is an individual contributor leadership role for scientists who enjoy building. You'll work at the intersection of applied research and production engineering—designing new algorithms, training and evaluating large models, developing novel post-training methods, and shipping...
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