Enterprise · RPA
Currently tracking 17 active AI roles, with 63 new openings in the last 4 weeks. Primary focus: Agent · Engineering. Salary range $200k–$290k (avg $235k).
UiPath has 31 active AI-related job listings. The majority of these roles, 65%, are focused on agents. Engineering is the dominant function, with 23 roles. The company is hiring for roles related to agent orchestration, model serving, and tool use. Over the last 30 days, UiPath posted 18 new AI roles, representing an 18% decrease compared to the previous 30-day period.
UiPath currently has 31 active AI-related roles in our index. The most common open titles are: Principal Product Manager (2), Principal Software Engineer (2), Principal Software Engineer, Site Reliability (2), Senior Software Engineer- Document Understanding (2), Data Scientist. Most positions are in Engineering and Product.
UiPath's active AI hiring is concentrated in: agents (68%), serving infrastructure (19%), application (6%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
UiPath is hiring AI talent in: United States (9 roles), Romania (9 roles), India (6 roles), United Kingdom (3 roles).
Job postings at UiPath most frequently mention: Statistics, Software Engineering, Orchestration, Agentic Systems, Content Generation.
In the past 30 days, UiPath has posted 21 new AI-related roles. That is a +62% change versus the prior 30 days (13 → 21).
| Title | Stage | AI score |
|---|---|---|
| Principal Software Engineer- Document Understanding This role focuses on building and evolving a cloud offering for training, deploying, and consuming ML models within the Document Understanding platform. The engineer will create and improve features for custom model training, document splitting, classification, and data extraction, impacting the platform's scalability, quality, performance, and reliability. | Data | 5 |
| Senior Software Engineer- Document Understanding Develop and evolve a cloud offering for training, deploying, and consuming ML models at scale within the Document Understanding platform. Focus on creating and improving features for custom model training to split, classify, and extract data from documents, impacting platform scalability, quality, performance, and reliability. | Data |
| 5 |