Software Engineer

We're looking for a Senior Software Engineer to help us build Nebius Serverless AI — our GPU-native platform for deploying inference endpoints, batch jobs, and AI workloads without managing infrastructure. Customers run production AI on it today; the mission now is to make it the fastest, cheapest, most reliable place to ship GPU workloads on the planet. This is a senior, high-ownership position on a fast-evolving service. You'll set the technical bar: own the architecture of the parts you ship, drive the hard design decisions, and raise the standard of everything around you through code reviews, design reviews, and the way you operate. The work is end-to-end — from cold-start latency in the runtime to the API contract customers integrate against. You'll work from our office in Amsterdam or London, hybrid. In this position, your responsibility will be to: - Design and build core components of the Serverless platform — the control plane, scheduler, runtime, autoscaler, and the APIs customers integrate against. - Own the hardest engineering problems on the service: cold-start latency, GPU scheduling under contention, multi-tenant isolation, fair-share quotas, request routing at the edge. - Set the technical direction for the areas you own — drive architecture decisions, write the design docs, and align the team behind the chosen approach. - Raise the engineering bar across the team through code reviews, design reviews, and example — the kind of senior presence that compounds. - Run the service like an SRE: define SLOs, build observability, lead incident response, and make sure every postmortem changes the platform, not just the runbook. - Work directly with customers on architecture reviews, performance escalations, and the production problems that don't fit a ticket template. - Partner closely with Product, GTM, and infrastructure teams to translate customer needs into a coherent technical roadmap. We expect you to have: - 7+ years of professional software engineering experience, with a track record of shipping production distributed systems at scale. - Excellent knowledge of Golang, or you are ready to quickly switch to it. - Deep experience with Kubernetes and container orchestration — you've operated it in anger, not just deployed it. - Strong distributed-systems instincts: consistency vs. availability trade-offs, queueing, backpressure, retries, idempotency, multi-tenancy. - Experience designing and operating high-throughput, low-latency services — you know where the milliseconds go and how to get them back. - A history of being the engineer others look to on hard problems — driving design discussions, unblocking teammates, and shipping the thing nobody else wanted to touch. - Ability to write reliable code and dig into complex problems. - Teamwork-oriented approach. It would be an added bonus if you had: - Experience building serverless or function-as-a-service platforms (Knative, AWS Lambda, GCP Cloud Run, Cloudflare Workers, Modal, Replicate, Together, Fireworks, Anyscale, or similar). - GPU scheduling experience — Kubernetes device plugins, MIG, MPS, time-slicing, NVIDIA GPU Operator. - ML inference experience — vLLM, TensorRT-LLM, Triton Inference Server, SGLang, model loading and warm-pool strategies. - Cold-start optimization at the runtime, image, or snapshot level (FireCracker, gVisor, checkpoint/restore, image streaming). - Experience writing Kubernetes operators (Go + controller-runtime / kubebuilder). - Contributions to relevant open-source projects in the serverless, scheduling, or inference ecosystems.