Senior Software Engineer | Kubernetes Automation

Opis oferty

• Competitive salary (€6,500 – €9,000 gross, depending on the level of experience)
• Enjoy a flexible, remote-first global environment.
• Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology.
• Equity options.
• Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks.
• Spend 10% of your work time on personal projects or self-improvement.
• Learning budget for professional and personal development – including access to international conferences and courses that elevate your skills.
• Annual hackathon to spark new ideas and strengthen team bonds.
• Team-building budget and company events to connect with your colleagues.
• Equipment budget to ensure you have everything you need.
• Extra days off to help maintain a healthy work-life balance.

Wymagania

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, Slovenia, and Ukraine.

• 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.

Zakres obowiązków

• Design and build distributed systems that operate Kubernetes infrastructure autonomously at scale.
• Write production Go services that interact with AWS, GCP, and Azure APIs for real-time cloud resource management.
• Own features end-to-end: from design through implementation, testing, and production rollout (most projects ship in 1-4 weeks).
• Debug complex production issues across cloud providers, Kubernetes clusters, and distributed services.
• Collaborate with product and other engineering teams to solve problems that don’t have textbook solutions.
• Work with time-series data, cloud provider APIs, and Kubernetes control plane internals.
• Contribute to product direction and backlog – shape what gets built next, not just how it’s built.
• Participate in on-call rotation for teams that require it.