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UiPath is seeking a Principal Applied Scientist to lead research and development of frontier AI systems that combine foundation models, multimodal reasoning, agentic workflows, and post-training techniques. This is an individual contributor leadership role at the intersection of applied research and production engineering.
You will design new algorithms, train and evaluate large models, develop novel post-training methods, and ship systems that reach enterprise customers. The Advanced Machine Learning organization is building large multimodal foundation models capable of understanding enterprise software, reasoning over complex workflows, planning long-horizon tasks, and autonomously interacting with digital systems.
Key responsibilities include:
- Leading R&D of large-scale foundation models and agentic AI systems for enterprise automation
- Designing novel approaches for post-training, model alignment, reinforcement learning, preference optimization, and synthetic data generation
- Developing scalable evaluation frameworks measuring reasoning, tool use, planning, and autonomous task completion
- Building production-quality AI systems combining LLMs, multimodal models, retrieval, planning, memory, and external tool use
- Driving innovations in long-context reasoning, workflow generation, autonomous software interaction, and multi-agent orchestration
- Designing large-scale experiments and iterating using data-driven evaluation
- Collaborating with engineering and product teams to transition research into production
- Mentoring scientists and engineers while raising technical standards
- Influencing UiPath's long-term AI strategy through technical leadership
Ideal candidates have deep expertise in foundation model training, LLM post-training, RLHF, preference optimization (DPO, PPO, GRPO), reward modeling, synthetic data generation, model evaluation, agentic reasoning, tool use, function calling, long-horizon planning, multimodal learning, RAG, distributed training/inference, large-scale PyTorch systems, and model alignment. A PhD or equivalent research experience in CS, ML, AI, Statistics, Robotics, or related field is preferred, along with significant experience building and deploying production AI systems based on large language models or multimodal foundation models.
