Platform Engineer

About the Role

Build the infrastructure that our AI agents run on. You’ll design systems that the rest of the team builds on, making pragmatic early‑tooling decisions that scale. As a foundational role, you’ll define the distributed‑system architecture and choose appropriate tooling, balancing simplicity with scalability.

Responsibilities

• Build and operate the runtime infrastructure that powers our AI agents in production, the systems that customers depend on.
• Design for concurrency and failure, including queuing, retries, backpressure, and idempotency, so agent workflows stay reliable under real‑world load.
• Own production reliability: latency budgets, error budgets, capacity planning, and incident response.
• Instrument the stack with tracing, structured logging, and alerting so problems surface before users notice them.
• Own the deployment pipeline end‑to‑end: CI/CD, preview and staging environments, and the path from local dev to production.
• Build internal tooling and abstractions that let the product team ship faster without worrying about infrastructure.

You Might Be a Fit If You…

• Have experience owning infrastructure end‑to‑end at a startup, where you picked the tools and lived with the consequences.
• Understand distributed systems deeply enough to know what managed platforms are doing under the hood, and where their abstractions will break.
• Care about developer experience: deployment pipelines, local dev setups, error messages, and the small things that compound into engineering velocity.
• Prefer to automate yourself out of toil rather than accept it.
• Want to lay the foundation at a company where infrastructure decisions directly shape the product.

What You Bring

• Strong understanding of distributed systems: concurrency, eventual consistency, event‑driven architectures, and failure modes, even if implemented with modern serverless tools rather than raw AWS primitives.
• Experience with CI/CD design: multi‑environment pipelines, automated testing gates, preview deployments, infrastructure‑as‑code.
• Hands‑on experience with platforms like Vercel, and an honest sense of where they scale and where they don’t.
• Familiarity with workflow orchestration tools like Inngest, Temporal, or Step Functions, and the queuing and retry semantics underneath them.
• MongoDB experience: schema design trade‑offs, indexing strategy, and performance tuning as data and query patterns evolve.
• Strong observability practice, ideally with Datadog, including distributed tracing, custom metrics, and SLO‑based alerting.
• Adequate AWS fluency (ECS, Lambda, SQS, IAM) to know what you’d reach for when managed tools hit their limits.
• High‑agency mindset and comfort operating independently in a fast‑changing environment.

Location & Logistics

• Berlin office preferred, but open for hybrid setups.
• Annual offsite (last year we took the team for 4 weeks to San Francisco).
• Start: As soon as possible.

What We Offer

• Top‑of‑market compensation and meaningful early equity.
• Direct access to real users and fast feedback loops.
• A highly experienced team with full trust and autonomy.
• The opportunity to shape the foundations of a company and product from day one.
• A mission that matters: building intelligent systems to power the future of global trade.