Top Tech Jobs & Startup Jobs in London

Lead EMEA go-to-market for Physical AI by sourcing executive-level pipeline, accelerating top accounts, building EMEA-specific playbooks, enabling regional sales, and converting lighthouse wins into sustained cloud consumption across robotics, autonomy, simulation, and related AI-native industries.

The Senior Software Engineer will build indexing systems and data pipelines for an agent-native search platform, focusing on data ingestion, processing, and organization for AI consumption.

The Senior Software Engineer will develop low-latency, high-throughput systems for a novel search engine. Responsibilities include optimizing request flows, ensuring reliability, and collaborating with indexing and ML teams.

Serve as an embedded IT risk and controls partner to engineering and technology teams, scaling IT SOX and ITGC frameworks across cloud, platform and corporate systems. Design and implement scalable controls, lead SOX readiness and testing, drive remediation, assess application and automated controls, leverage automation and analytics to improve evidence quality, and liaise with Finance and external auditors to maintain audit readiness and control health.

Lead Nebius’s M&A and strategic investment activities: source and evaluate targets, run diligence, build financial models and business cases, drive deals to close, and support post-merger integration while managing stakeholder relationships.

The Corporate Development Associate supports capital formation, manages financial analysis, executes financing transactions, and collaborates with stakeholders on growth initiatives.

Develop and optimize GPU clusters and InfiniBand networks for hyperscale HPC environments. Responsibilities include troubleshooting hardware and infrastructure issues, integrating new GPUs through Kubernetes, QEMU, and KVM, managing GPU devices and InfiniBand fabrics, tuning performance, and improving automated monitoring and fault resolution.

Conduct frontier applied research on efficient AI model architectures, including sparse attention, long-context memory, selective computation, dynamic inference, reasoning, and continual adaptation. Formulate research questions, design rigorous experiments at meaningful scales, develop efficient implementations, publish findings, contribute to open-source tools and models, mentor researchers, and help direct the research stream.

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.