Overview As Principal Applied Scientist at UiPath, you lead research and development of frontier AI systems that integrate foundation models, multimodal reasoning, and autonomous workflows to operate enterprise applications. You bridge applied research and production engineering, training large models, testing post-training methods, and shipping production-ready AI for enterprise customers. You work with cross-functional teams to translate research into scalable systems and evaluate them with data-driven methods. You will shape UiPath's long-term AI strategy and mentor teams, influencing product direction and technology choices. This role offers the chance to push general intelligence for enterprise,
Responsibilities- 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
- Develop scalable evaluation frameworks that measure reasoning, tool use, planning, and autonomous task completion
- Build production-quality AI systems that combine LLMs, multimodal models, retrieval, planning, memory, and external tool use
- Drive innovations in long-context reasoning, workflow generation, autonomous software interaction, and multi-agent orchestration
- Design large-scale experiments, analyze model behavior, and iterate using data-driven evaluation
- Collaborate closely with engineering and product teams to transition research into production systems
- Mentor scientists and engineers while raising the technical bar across the organization
- Influence UiPath's long-term AI strategy through technical leadership and scientific innovation
Key requirements- PhD or equivalent research experience in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Robotics, or a related discipline
- Significant experience building and deploying production AI systems based on large language models or multimodal foundation models
- Demonstrated ownership of end-to-end ML systems-from research and experimentation through production deployment
- Strong programming skills in Python and deep experience with modern ML frameworks such as PyTorch
- Experience training or fine-tuning large models using distributed compute infrastructure
- Strong publication record or demonstrated technical leadership in applied AI is highly valued
- Excellent communication skills and the ability to influence technical direction across organizations
- Excellent communication and collaboration
- Ability to influence technical direction
- Ambitious, proactive, and results-oriented
- Foundation model training
- LMM post-training
- Reinforcement Learning from Human Feedback (RLHF)
