Senior/Staff Platform Engineer

You will be making system design decisions explicit and then making them hold. That means codifying golden paths, building the shared primitives and APIs that product engineers work on top of, owning the architecture of the system as a whole, and establishing what reliability actually needs to be before an incident establishes it for us.

What you'll do

• Make the easiest way for product engineers to do something also the most secure, reliable and scalable way by default. Build the shared primitives, libraries and APIs that hide complexity and carry quality and observability standards with them.
• Infrastructure as code by default, from cloud resources through to dashboards and alerts. Provision, run and tune the Kubernetes cluster and cloud footprint, and keep CI fast as the deploy rate grows.
• Define SLIs for the workloads that matter, build SLOs on top of them, and make the alerting high-signal enough that people trust it. Drive down AI and infrastructure spend.
• Keep the internal foundations secure by default: IAM, dependency management, secrets. Own the authentication and authorization stack, including ReBAC models covering both humans and agents.
• Keep product engineers and their agents on the paved road by making sure the documentation and agent guidelines they need are in place.

What you'll need

• Have designed, built and operated distributed systems end to end, and enjoy understanding how every part interacts with the rest.
• Are strong at backend software engineering and at DevOps/SRE, and don't think of those as separate jobs.
• Write design docs, RFCs, ADRs, postmortems.
• Reach for simple, boring solutions first, can tell essential complexity from accidental, and know which corners are safe to cut and which ones compound.
• Have scaled Postgres or another relational transactional database under real load, run production Kubernetes, and built observability rather than inherited it: SLIs, SLOs, distributed tracing.

Nice to have

• Durable workflow orchestrators like Temporal, and background job and queue processing generally.
• Infrastructure for LLM-based products or agentic systems.
• Audit, finance, compliance, or another high-accuracy domain.
• Azure and/or GCP.

What they offer

• Meaningful equity and competitive salary.
• Real influence on strategy in a small team of excellent engineers with high autonomy.
• Fast, ambitious team where decisions get made in hours, not weeks.
• Mission building intelligent systems for a $200bn industry from Berlin, with AI at the core.