Lead AI Scientist for Autonomous Enterprise Automation

Socket.dev; Bellevue (WA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

UiPath is seeking a Principal Applied Scientist to 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.

You will lead research at the intersection of applied research and production engineering—designing new algorithms, training and evaluating large models, and shipping systems to enterprise customers.

Qualifications

• PhD or equivalent research experience in CS/ML/AI or related discipline.
• Significant experience building and deploying production AI systems based on LLMs or multimodal models.
• Ownership of end-to-end ML systems from research to deployment.
• Strong Python programming and practical experience with ML frameworks (PyTorch).
• Experience training or fine-tuning large models using distributed compute.

Responsibilities

• Lead research and development of large-scale foundation models and agentic AI systems for enterprise automation.
• Design approaches for post-training, model alignment, RLHF, and synthetic data generation.
• Develop scalable evaluation frameworks for reasoning, tool use, planning, and autonomous task completion.
• Build production-quality AI systems that integrate LLMs, multimodal models, retrieval, planning, memory, and external tools.
• Mentor scientists and engineers, raising technical bar across the organization.

Skills

Foundation model training

LLM post-training

Reinforcement Learning from Human Feed

Python

PyTorch

Distributed training

Model alignment

Azure AI

Kubernetes

Education

PhD or equivalent research experience

Tools

PyTorch

JAX

CUDA

LangGraph

Job description

UiPath is seeking a Principal Applied Scientist to 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.

You will lead research at the intersection of applied research and production engineering—designing new algorithms, training and evaluating large models, and shipping systems to enterprise customers.