Top Tech Jobs & Startup Jobs in London

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Lead the Managed Kubernetes Core team while remaining hands-on with backend engineering. Responsibilities include writing and reviewing code, leading technical projects, maintaining technical vision, participating in on-call and incident response, coaching 4–8 engineers, and collaborating on product strategy. The role focuses on Kubernetes control planes, cluster autoscaling, platform integrations, add-ons, GPU infrastructure, and managed cloud services.

Build and operate stateful streaming pipelines and Go services that process usage events into accurate, near-real-time billing data. Responsibilities include ingestion, validation, enrichment, pricing, storage integration, usage APIs, billing exports, failure recovery, idempotency, exactly-once processing, reconciliation, scalability, observability, and deployment ownership.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Design and build the Capacity and Quota Management control plane for GPU resource allocation. Implement reservation engines, quota control algorithms, APIs, rebalancing, and auto-reclamation; ensure consistency, reliability, and scalable distributed backend services.

Design and build distributed backend services for the cloud monetization platform, focusing on correctness, consistency, scalable microservices, distributed transactions, and reliable asynchronous workflows. Influence system architecture, own service implementations, and collaborate across teams to support billing, reservations, payments, and revenue flows.

Build fault-tolerant, reliable cloud backend services for a hyperscaling AI platform, including compute, storage, networking, managed Kubernetes, databases, billing, monitoring and ML services.

Build and operate a managed PostgreSQL service for AI workloads: develop control plane and lifecycle automation in Go, tune PostgreSQL internals and ecosystem tooling, create migration tooling, integrate vector search, run SRE practices (SLOs, observability, incident response), and work directly with customers on architecture and escalations.