(Senior) Product Engineer - m/f/d

Product Engineers own a customer problem and its outcome from discovery through production. You speak with users, decide what the product should do, build the solution across the stack, ship it, and use production feedback to determine what happens next.

Being close to users gives you substantial product-shaping power. You are expected to distinguish the underlying need from the initial request, form a point of view, and make decisions about scope, interaction, architecture, and sequencing. The role is measured by what changes for users and the business, not by how many tickets move to done.

We like the way Linear builds product: a clear point of view, fast iteration, and a consistently high standard in the final software. We want the same care and coherence in a more complex setting, where AI behavior is probabilistic and enterprise requirements are part of the product rather than an afterthought.

• You have several years of experience, typically 3 to 6, building and shipping full-stack product software across TypeScript, React, Node.js, databases, and queues. You can explain what changed for users or the business because of work you personally owned.
• You are curious about how customers actually work. You investigate beyond the initial request, test assumptions, distinguish an important need from a feature request, and develop a point of view about the right solution.
• Given an underspecified problem, you identify what matters, decide what to cut, and find simple, non-obvious ways to move from ambiguity to shipped software. You can explain what you deliberately chose not to build and why.
• You understand the practical behavior of large language models, including context windows, tool calling, streaming, provider differences, and failure modes. You use AI tools to explore approaches and multiply your output while remaining responsible for every change that ships.
• You care about the people around you. You share context, ask for input, disagree directly and respectfully, and help other engineers produce better work.

This is an experienced individual-contributor role. You should be able to independently investigate an unfamiliar problem, design a solution, ship it, and operate it in production. We generally expect this judgment to come from at least three to four six of full-time experience working on production systems.

Langdock is the AI platform used by more than 10,000 companies to give employees secure access to the leading AI models, to build and share agents and to automate repetitive workflows. We have grown past $40M ARR while remaining a small team, and we care deeply about operating efficiently across the entire company.

For many enterprises, Langdock is becoming the place where most of the net-new work is produced. As people and agents create more documents, analyses, decisions, and automations inside AI interfaces, the context and data behind that work accumulate within Langdock. This gives us the opportunity to earn a larger role in their technology stack by building a platform they choose to rely on.

Our ambition is to build that platform for European enterprises while preserving their control over data, model providers, and deployment environments. We have made meaningful progress at the application layer, but much of the foundation beneath it still needs to be built.

You can watch the Meet the engineering team video to get a feeling for how we work.

Product Engineers turn new AI capabilities into products that fit how enterprises actually work. This includes evolving our core products, inventing new interaction models for people and agents, making probabilistic systems understandable and governable, and bringing those capabilities into the tools and workflows customers already use.

• Chat and Agents. Evolve the core Langdock experience where people work with models and agents. Design how users provide context, work with files, call tools, create and share agents, and turn model output into completed work. The product must make streaming responses, tool execution, latency, and failure feel clear and dependable without exposing users to the complexity underneath.
• New interfaces for agentic work. Design how people steer multi-step work, review intermediate results, approve actions, edit outputs, and recover from failure when established interface conventions do not exist yet. This includes moving beyond chat toward interfaces for producing documents, analyses, applications, and other structured work together with agents.
• Making agents trustworthy at enterprise scale. Build governance and evaluation capabilities that help workspace administrators discover, test, approve, configure, and manage agents without making them harder for employees to use. Product questions include how to compare agent versions, diagnose regressions, manage permissions, and introduce control at scale without slowing down experimentation.
• Organizing the work agents produce. Develop Library, the system that brings attachments, generated outputs, and connected knowledge together across Langdock. Help people and agents find the right context, understand where information came from, reuse previous work, and preserve permissions as content moves through the platform.
• Automating long-running work. Extend Workflows, where agents call tools, pause for human input, produce structured outputs, retry safely, and continue across business processes. Build the interfaces that let users compose these workflows, understand their current state, intervene when necessary, and diagnose what happened when a run fails.
• Bringing Langdock into existing workflows. Build Excel and Outlook integrations that make AI useful inside the tools where people already work. This requires understanding how customers work with spreadsheets and email, choosing the right context and actions, and delivering a reliable experience within the APIs, permissions, and interface constraints of another product.

You will start in one area based on your experience and the team's priorities, then stay close to its users and own the outcome in production.

• TypeScript, React, Next.js, and Tailwind CSS in one Bazel monorepo
• React Native and Expo for mobile
• Node.js services and workers
• PostgreSQL with Prisma; Redis with BullMQ
• Our own AI engine across OpenAI, Anthropic, Google, Mistral, Bedrock, Azure, and open-source models
• Linear for planning and GitHub for code review

You should be familiar with most of this. We trust you to pick up the rest quickly.

This is an in-office role at Greifswalder Strasse 212 in Berlin. We work together in person because it helps us build trust, develop shared context, and make decisions quickly.

Most engineers start around 8:30. We usually eat lunch together, and dinner is available for people who stay later. Running and gym are part of the routine for many of us.

You need an existing right to work in Germany. We do not currently sponsor visas.

The salary range for this role is €135,000–€200,000 gross per year, depending on level and scope. All roles include equity. Salaries are tied to levels, not negotiation.

We will figure out the right level together based on your experience and scope. Levels are about the work you own, not your title or years of experience.

• We operate with high trust and autonomy in squads of 3 to 4 engineers. A squad owns its roadmap, prioritization, technical decisions, and operation in production. Engineers are expected to find the context they need, ask for input when it improves the outcome, and move work forward without waiting for every next step to be assigned.
• We align asynchronously before scheduling a meeting. Product requirement documents (PRDs) define the user problem, intended outcome, and constraints. Design documents make architectural boundaries, tradeoffs, failure modes, migrations, and rollouts explicit. People read and challenge the thinking asynchronously; once the context is shared, a short in-office discussion or whiteboard session usually resolves the remaining questions quickly.
• We optimize for leverage. Engineers choose the AI tools that work for them, supported by clear ticket context, focused branches, automated tests, and AI review before human review. We also invest in observability, migration tooling, automated recovery, and runbooks so recurring product maintenance does not depend on someone remembering a manual step.
• The engineer who ships a change owns it in production. If something breaks, you lead the fix.