Jobs at Nebius

Serve as the primary technical advisor for strategic GPU cloud customers: troubleshoot complex AI/ML issues, optimize GPU performance, guide deployments at scale, collaborate with sales and product teams, and translate customer needs into product feedback and solutions.

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.

Manage IT infrastructure projects and change requests in live data centers, including GPU servers, networking, compute, and storage systems. Coordinate engineers, vendors, operations teams, and stakeholders; track risks, dependencies, issues, and documentation; lead technical meetings; resolve operational problems; and support automation, process improvements, and deployment activities. Occasional business travel is required.

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.

Maintain and scale DevTools SRE systems: improve user-facing developer workflows, build fault-tolerant self-healing architecture, optimize performance, modify open- and closed-source tools (GitLab, TeamCity), and support users while measuring improvements via metrics.

Serve as primary technical advisor for strategic GPU cloud customers, guiding design, deployment, and scaling of large-scale ML training and inference workloads. Troubleshoot complex AI/ML issues, optimize GPU performance, liaise with sales and product teams, and drive customer-aligned feature requests and solutions.

Own the full sales cycle across a new UK commercial territory, from prospecting and pipeline generation through close. Build relationships with technical buyers, product leaders, and business stakeholders; lead discovery, product demonstrations, technical evaluations, and proof-of-concepts. Apply MEDDPICC, maintain accurate CRM forecasts, create repeatable sales playbooks, and provide customer feedback to product and go-to-market teams. This founding hire will establish Tavily’s UK commercial presence and customer base.

Manage end-to-end UK immigration cases and coordinate global immigration processes. Serve as primary contact for employees and stakeholders, ensure compliance with UK law, maintain case records in Jira and HiBob, liaise with external providers, and oversee invoice processing and governance to mitigate immigration risk.

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.

Build and scale AI-driven automation solutions using LLMs and agent frameworks. Design multi-step workflows, integrate AI with internal systems (HR, finance, legal, ticketing), automate document processing and data extraction, and train internal teams. Collaborate with technical leads, product, and IT to deliver practical, production-ready automation and iterate quickly.

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.

Lead research on model architectures to improve attention, memory, and efficiency. Design and run experiments at scale, develop methods to reduce training/inference cost, collaborate with engineering for implementation, publish and open-source work, and mentor other researchers to shape the research direction.

The Senior Site Reliability Engineer will ensure fault-tolerance and uninterrupted operations, using innovative cloud technologies, alongside improving CI/CD processes.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Design and implement large-scale browser-based rendering infrastructure to execute and extract structured content from JS-heavy web apps. Improve rendering quality, performance, cost, and throughput at internet scale and productionize rendering research.