About the role
Hello! Do you want to help us redefine how AI powers the future of Business Management software, not as an add-on, but as a core part of how people work, decide and grow ? We’re looking for engineers who: Treat AI as central to their craft, not a feature to bolt on later Want to build and ship, not debate and document Care about whether the thing they built actually worked — not what their title says. Ready to be part of the challenge? 🚀 At Factorial we're looking for a Product Engineer to help us build and scale our AI- Driven Talent Domain to the next level. As part of this team, which is in charge of providing tools to managers and HR managers in the areas of recruitment, performance, engagement, and trainings, you'll be at the forefront of developing tools and help to improve the way companies take care of their people, their most valuable asset. Ready to be part of the challenge? 🚀
About
the team 👥 Our Engineering team is the engine behind Factorial, crafting the tech that makes HR simple, fast, and reliable for thousands of companies. If you love solving hard problems and want to build something that truly matters, come build it with us. We're a team of 200+ Product Engineers building Business Management solution that actually works for the people using it. Our core stack is Ruby on Rails and React but the interesting work right now is what we're building on top of that: agentic workflows, LLM-integrated features, systems that behave probabilistically rather than deterministically. The mission of our Talent Domain is to help companies develop their employees, keeping them engaged and delighted at the same time, helping the company’s teams become the best versions of themselves. To do this, we provide tools to managers and HR managers in the areas of recruitment, performance, engagement, and trainings. But we know there is space for more! That's why we need talented people to go faster. What are we looking for? 🎯 You've shipped AI-powered features and know that evaluating them in production is the hard part, not building them. You think about reliability and user friction before model complexity. You're comfortable reviewing agentic workflows and have opinions about LLM architecture tradeoffs. Not just theoretical ones. You stay current: RAG patterns, agentic frameworks, what's actually working vs. what's hype and you apply that judgment to real product decisions. You treat AI features as hypotheses. Build, deploy, measure, adjust. You own that whole loop, not just the build part. You can break down non-deterministic outputs into something the rest of
