About The Position

As a Principal Software Engineer on UiPath’s Growth team, you will investigate why people adopt, continue using, or stop using UiPath products, then build and ship changes to improve those outcomes. This is an engineering-first role involving production code across the platform, from the initial landing page through users’ first published automation. You will also contribute product thinking by selecting problems, defining success measures, and using data to validate results. You will be a founding engineer on a high-visibility team, working directly with senior engineering and product leaders. The role offers the freedom to independently shape and build ideas using UiPath’s agentic automation platform, with results reflected in metrics followed across the company.

Requirements

  • Principal-level engineering experience.
  • Recent hands-on experience shipping full-stack production code, such as TypeScript, React, and backend services, or similar technologies.
  • Uses coding agents every day and can describe their workflow in detail.
  • A track record in growth, activation, or funnel work at a product-led company, with metrics personally improved.
  • Strong product sense, including identifying problems, proposing solutions, and determining how to measure success without waiting for a specification.
  • Ability to move quickly with a small footprint and deliver results without a large team.
  • Clear written and spoken communication, including the ability to make a case to senior leaders.

Nice To Haves

  • Growth experience at an enterprise product-led company where self-serve users become business customers.
  • Experience optimizing for AI agents as users, such as creating agent-friendly documentation, APIs, and discovery.
  • A public presence through talks, posts, open source, or other content developers read.
  • Familiarity with automation, workflow, or developer-platform products.

Responsibilities

  • Help define the growth metrics owned by the team, including sign-up, trial activation, and upgrade.
  • Analyze product telemetry and public signals such as community forums, social media, and video comments to identify where users get stuck and why.
  • Turn findings into testable hypotheses, then build, ship, and A/B test solutions.
  • Work across any part of the codebase needed to implement a fix rather than handing it off to another team.
  • Use AI coding agents to move ideas from concept to production quickly.
  • Communicate each experiment to engineering, product, design, and leadership, explaining its rationale, success criteria, and results.
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