Product Engineer, Conveo AI

• 5+ years of experience building applications or tooling that automate human workflows
• Full-stack experience across the board: API design, databases, async workflows and background jobs on the backend, and clean, user-friendly UX on the frontend
• Being AI-native. An early adopter of AI tooling, like agentic coding and software factories, with hands-on experience building AI-centric products through prompt engineering, LLM APIs, etc
• Extensive client-facing experience turning customer problems into solutions people actually adopt
• Proven track record of leading projects from concept to launch
• Excellent written and verbal communication skills in English with a keen eye for detail
• A collaborative mindset, able to work autonomously in a small, fast-paced team

Desirable

• The internal apps engineering team builds products that give the humans at Conveo maximum leverage
• As the cost of building software falls to zero, we craft opinionated internal tools that fit exactly how we work, from full apps (CRM, account management, recruitment), agents that run across the business, and the tooling that lets anyone at Conveo automate their own workflows
• This team sits inside Engineering and is the group actually building these apps day to day. There are two of us on it today - you'd be the third. 👋
• We need a Senior Product Engineer who works backwards from the experience. This is someone who starts with the user's problem, not a spec, and owns everything between that conversation and the code that ships
• You shape what gets built and how, contributing to product direction with real technical and strategic impact
• Build the internal tools the team uses every day and can't imagine working without. For us the interface is mostly Slack and React: the agents people talk to and the apps they click through, built in TypeScript. And you don't stop there
• From API and MCP design to infrastructure on Vercel, AWS, Supabase, and BigQuery, plus the agents that work across the stack, you own complete features end to end, including the AI-powered ones built on LLM APIs, prompt engineering, and async pipelines
• You're embedded in the teams you build for. When a sales rep, a CS manager, or a recruiter has a need, an engineer hears it, interprets it, and solves it
• You hold weekly one-on-ones with the people who actually use your tools, form a clear point of view on what to build, translate their feedback into scoped decisions, and follow through
• You reach for agentic solutions first, using AI to reason, build, review, and test at every stage. Building our own internal stack instead of buying third-party tools means recreating, for our own purposes, software others spent twenty years building, so staying on the bleeding edge of AI is how we close that gap fast
• And because these are internal apps, we can lean on AI far more aggressively than an external product could: we're close to our users, feedback is immediate, and the upside of a new feature usually outweighs the cost of a bug we can fix fast. The bias is toward speed and learning. We're looking for people who thrive on that edge and help us continuously improve our software factory