Systems Engineer - m/f/d

Where Europe's enterprises adopt AI 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.

Systems Engineers solve our most complex technical problems inside and across Langdock's shared services when existing building blocks or standard architectures are insufficient. These are problems where correctness under failure, performance, scale, security, and cost interact. This is a project-oriented role rather than ownership of one permanent layer. You might spend a period on distributed execution, then move into storage architecture, workload isolation, or model serving as company priorities change. The constant is sustained investigation of unfamiliar systems and responsibility for turning that understanding into a production system. Systems Engineers own the difficult mechanism and the measurable improvement it creates, whether in correctness, capability, performance, reliability, or cost. They operate what they build, while the Platform owner remains accountable for the service contract and long-term lifecycle around it. What you might work on The systems agenda includes: Distributed state and execution. Design systems that remain correct when work is concurrent, long-running, retried, resumed, or moved between processes. Define explicit invariants for ordering, idempotency, recovery, and tenant isolation. Storage and data systems. Improve how large volumes of customer and agent-generated data are stored, versioned, moved, and recovered while preserving consistency, data residency, and predictable performance. Secure compute for agents. Develop the isolation, runtime, and scheduling mechanisms behind a shared sandbox service for untrusted code. The system needs persistent filesystems, deny-by-default networking, scoped mounts, fast startup, suspension, resumption, and predictable scheduling without weakening the security boundary. Inference systems. Improve the serving systems beneath the Model Gateway, optimizing throughput, latency, accelerator utilization, reliability, and cost. The work is measurement driven: understand the workload, identify the actual bottleneck, and decide where changes to serving, scheduling, caching, model format, or hardware create structural advantage. Resource scheduling and isolation. Make CPU, memory, storage, network, and accelerator capacity explicit so one workload cannot degrade another. Improve placement, admission control, backpressure, autoscaling, and recovery across deployment environments. Billing and usage metering. Build the concurrent metering mechanism behind the billing capability, accounting for heterogeneous units such as model tokens and sandbox compute time. It must stay correct when many workloads report concurrently, events arrive late or more than once, and long-running jobs reserve capacity before their final usage is known. This involves idempotent ingestion, atomic reservations, reconciliation, and auditable records. You will start with one focused problem based on your