Senior Software Engineer | Kubernetes Automation

What you'll be solving
A pod is mid-request. The node it's running on is about to disappear - Spot is pulling the instance, or Autoscaler has flagged the node as underutilized and marked it for removal. The safe move is to wait. The profitable move is to act now. Every team in this pipeline is built to close that gap: make the aggressive call and still guarantee nothing breaks.
That guarantee is the hard part. Deciding which nodes to kill, which pods to move, and which storage volume to resize is a genuinely hard optimization problem - several of them are P=NP-hard in the general case - and it has to run continuously, in production, faster than a human would ever attempt it by hand. Get it right and clusters run at half the cost. Get it wrong once and you've taken down something live.
This posting covers multiple teams working on different pieces of the same automation problem node placement algorithms and provisioning, deep integrations with tools like Karpenter, workload rightsizing (vertical and horizontal), moving running workloads between nodes without interrupting them, storage that scales itself, and GPU capacity optimization across clouds and regions. All of it runs in real time, in production, with no human tuning YAML in the loop.
This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Cyprus, Malta, Finland, Sweden, Ireland, Moldova, North Macedonia, Montenegro, Slovenia, and Ukraine.
Requirements:
Strong software engineering fundamentals: you design clean abstractions, write maintainable code, and can justify your architecture choices - not just make something work.
Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language.
Understanding of Kubernetes internals – autoscaling and networking.
You've personally driven a complex project end-to-end.
Experience with AI coding agents and agentic development workflows.
You've used observability tooling in production.
CI/CD and DevOps practices experience.
Strong English skills, both verbal and written.
Startup mindset: adaptable, proactive, and comfortable with ambiguity.
Nice-to-haves:
Deep hands-on experience with cloud platforms (AWS, GCP, or Azure) – including real understanding of how compute, networking, and storage work under the hood.
Deep knowledge of EKS, GKE, or AKS internals.
Container runtime experience CRI-O, runc, containerd.
Cloud storage depth – block storage, CSI, volume management, filesystem internals.
Low-level Linux experience: process internals, packet-level networking (NAT, iptables, conntrack, eBPF, SDN), filesystems, storage.
Low-level systems programming experience in C or C++.
Kubernetes or cloud-native OSS contributions.
Experience with AI coding agents and agentic development workflows.
Responsibilities:
Design and build distributed systems that operate Kubernetes infrastructure autonomously at scale.