Staff AI Engineer

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 Staff Engineers who want to lead that shift, people who: Think about AI as part of how they engineer, not a feature they add Get energy from shipping things and seeing them work in the real world Care more about what they build than what their title says Ready to be part of the challenge? 🚀

About

the team 👥 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 Operations Domain sits at the core of Factorial. This is where we build the systems that thousands of companies rely on every day to run their business, and they need to be accurate, compliant, and solid. Our teams own some of the most business-critical workflows we have: How employee time is tracked, planned, and managed How the employee journey is modelled from onboarding to offboarding How companies represent their organizational structure How

compensation and payroll data are calculated and trusted Our mission is to take operational complexity and turn it into something simpler: AI-powered experiences that help companies scale without losing sight of the humans behind the decisions. What are we looking for? 🎯 You have solid depth in both backend and frontend software engineering You have worked with cloud platforms like AWS, Azure, or GCP You have shipped real products to production, not just prototypes You understand APIs, cloud platforms, and system design well You are seen as leaders technically and culturally, and hold the bar high Your always push back on processes that don't serve

the team, from hiring to deployment You drive adoption of new abstractions and make sure others can actually use them You've built real AI-powered systems or workflows, not just tinkered with them You've shipped things that made a measurable difference, whether at work or on the side You lead key technical decisions and create things others across the company build on You communicate clearly in English; Spanish is a plus. What You’ll Be Doing? Define how we build AI at scale: We're still figuring out what great AI engineering looks like at Factorial, and that's part of the job. You'll help shape how we design, build, evaluate, and run AI-native product experiences, making sure they're reliable, compliant, and actually useful to the business. Lead Spec-Driven Development for AI : One of the things we care deeply about is not shipping blind. You'll push for a culture where teams define what "good" looks like before they build: expected behaviours, constraints, edge cases, success criteria. Then use those specs to move fast without breaking things. Build reusable AI foundations We don't want a patchwork of one-off AI experiments. You'll work on shared capabilities and abstractions that other product domains can actually use, built to scale, secure, and easy to govern. Design and evolve AI Skills: You'll drive the creation of modular, reusable AI "skills", components that can automate real workflows and take safe, traceable actions inside Factorial's ecosystem, especially where finance is involved. Establish robust evaluation frameworks: Getting AI to production is one thing; keeping it trustworthy is another. You'll build evaluation systems both automated and human-in-the-loop, so teams have a real signal on quality, safety, and reliability. Shape Retrieval-Augmented Generation strategies: You'll define how we ground our AI systems in data we actually trust. That means balancing probabilistic models with deterministic sources of truth, and making sure the whole thing scales without cutting corners on compliance or accuracy. Design human-in-the-loop and agentic workflows: The best AI systems we can build are ones where humans stay in control of what matters. You'll architect workflows that combine LLMs, rules, and user oversight: enabling automation while keeping things transparent and auditable. Own systems end-to-end: From the first conversation about a problem to production rollout and long-term maintenance, you'll own it. Not just the fun parts. Stay deeply connected to customers: The best technical decisions come from understanding real problems. You'll engage directly with customers to get that context, and bring it back into how we build. Partner with Product, Design, and Leadership: AI can't live in an engineering silo. You'll work cross-functionally to make sure it's woven into the product, not grafted on top, and you'll have a real voice in roadmap decisions. Raise the bar across the company: Whether it's mentoring, hiring, or pushing for better practices, you'll leave the engineering culture better than you found it