Product Engineer (EU/UK Based - Remote)

A lot of enterprise work still happens by hand. People copy-paste between spreadsheets, live in endless email threads and click through clunky legacy UIs. We started Duvo to end that for good.

Customers already trust us with business-critical processes, and our agents are automating them today. We're growing fast, and to win from here we need exceptional people. That's where you come in.

We're a team of 60, with 22 engineers, and we just raised a $32 million Series A led by Balderton Capital.

Duvo is the AI operations platform for large enterprises. Right now we're focused on retail and consumer packaged goods. Customers build AI agents in Duvo that do the work wherever it lives: SAP, spreadsheets, supplier portals, email, APIs, you name it. We're heavy on browser and computer use.

Business users say what outcome they want. The agents plan, act, ask for approval on exceptions and get better with every run. To help customers figure out what to automate, we also map their processes from interviews and internal docs, which gets our foot in the door. We start with the parts of a company we know best (category management, supply chain, finance ops), where we can show value fast, and expand from there to other functions and sectors.

Velocity is our moat. We ship fast and iterate even faster.

You'll own user-facing features and the systems behind them end to end, live in production and measured by customer impact. You connect technical choices to what customers actually get. Product engineers at Duvo are intense about delivering impact fast.

You also get to work directly with our enterprise customers. You'll deliver specific pieces for them and talk to them when it helps. That said, we don't expect you to be a polished presenter on day one.

You don't need retail or CPG expertise, but you should be curious about how our customers' businesses actually work.

These are the non-negotiables, and it's what we'll evaluate you on:

• Shipping and ownership. You've taken ambiguous requirements to production across the whole feature (UI, API, data, deployment), more than once. You don't wait for someone to tell you what to build next. You set your own scope based on customer problems and ship it within a week.

• Product judgment. You can scope an MVP, pick the metric that matters and kill work that isn't delivering. You think about what the user needs before what's technically interesting. You can explain the tradeoff between shipping fast and going deep, and you can tell us what your work was worth to the customer or the business, beyond how it works.

• AI-native. You use AI every day to build. Think coding agents, often several running in parallel in Claude Code or Codex. You still own the quality, also when the reviews and tests are AI-written.

• Product quality. You care how a feature looks and feels. When something is off, you notice and you fix it.

• Collaboration. You work across functions. You align early with product and design, pull in people from other teams whenever you're missing context, and unblock yourself and others.

• Have designed and run distributed systems that scale, with a good sense for security and reliability in production.

• Have built LLM products: features on top of LLM outputs or AI agents (parsing agent responses, human-in-the-loop flows), ideally in production with eval design, prompt engineering, safety controls and cost optimization. You won't be training models either way.

• Have designed interfaces for AI-assisted workflows, like confidence signals.

• You need a lot of hand-holding, or figuring things out yourself doesn't give you energy.

• You mostly want to work on infrastructure. Check out our SRE and AI Platform Engineer roles instead.

• You only want to do frontend without owning the full feature path.

We're looking for people based in Europe, in European time zones, so we overlap for most of the working day.

• TypeScript first: Next.js, React, Tailwind, Fastify, Kysely (PostgreSQL), Zod

• GCP, Datadog

• Latest AI primitives (Anthropic)

You don't need to know all of it. You should be excited to pick it up fast.

These are real tradeoffs we made:

• Initiative-driven. We organize around customer problems instead of org charts. Problems come from product feedback, competitive analysis and talking to customers directly. Then we prioritize, build and ship every week.

• Customer-obsessed. We work on real problems for real customers, not hypothetical ones.

• Iterative by default. We ship small, learn fast and don't get attached to yesterday's code. Things break sometimes, and we fix forward.

• AI first. We use AI to go faster and spend human time where it really matters. If a tool can do it, a person shouldn't.

• Direct feedback. We tell each other right away what to improve. It can feel uncomfortable, and we think it's worth it.

• Autonomy with accountability. We trust people to make the call, and we judge them on outcomes.

• A competitive salary and a generous ESOP allocation in a fast-growing company, fresh off our Series A.

• Unlimited spend (within reason) on the AI tooling of your choice.

• If you're remote, we reimburse your co-working space if that's where you feel most comfortable.

• Travel: frequent visits to our Prague office, offsites (our upcoming engineering retreat includes a day at the Porsche Experience Center Hockenheimring) and conferences (we recently sponsored Lisbon AI).

• Autonomy to do your best work. Want to meet someone to learn from? Set it up. Want a mentor? Go get one.

• A real AI product used by enterprise customers. No demos, no internal tools.

• Many AI modalities in one product: voice agents, computer and browser use, reasoning, and long-running agents. Applied AI, not research.

• A sharp, motivated team that values ownership and candor.

We respect your time and move fast:

1. Application review. We read your application against what's listed under "What we're looking for".

2. Discovery interview (30 min). A conversation with our hiring manager about your background, how you think and how you communicate.

3. Technical interview (60 min). Closer to an architecture and design conversation, and it may turn into building something live with AI.

4. Trial day (1 day). You get an ambitious problem in the morning, work on it with the team and present what you built at the end of the day.

5. Offer. If the trial day goes well, we move quickly to an offer.