Jobs at Nebius

Own the vision, roadmap, and backlog for commercial agreements and pricing mechanics in Nebius’s billing platform. Design billing capabilities, develop revenue-driving pricing models, productize recurring enterprise deal patterns, and partner with Sales, Finance, Legal, Operations, and Engineering. Lead discovery, requirements, delivery, documentation, enablement, and success metrics while modeling revenue and margin impacts.

Lead the global employee relations and performance management strategy, including policies, governance, complex case oversight, investigations, disciplinary and grievance processes, and organizational change consultation. Build effective works council and employee representative relationships, manage employment-law and reputational risks, advise senior leaders, strengthen manager capability, and use case data to identify trends and improve organizational practices.

Lead a Platform Experience product group to define vision, strategy, and metrics; manage and grow PMs; create a consistent API-first developer experience across CLI, SDKs, Terraform and docs; reduce time-to-first-value from PoC to production; evolve quotas, capacity and multi-tenant resource models; and unify customer notifications and communications.

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 production optimization of LLM and VLM inference systems, improving latency, throughput, memory efficiency, GPU utilization, quality, reliability, and cost. Deploy and benchmark inference engines, build compression and quantization workflows, implement acceleration techniques, diagnose serving bottlenecks, and collaborate with kernel, platform, research, product, and customer teams.

Design and maintain presentation and customer-facing asset systems, including templates, guidelines, sales materials, PDFs, diagrams, charts, and infographics. Translate complex technology topics into clear, persuasive visual narratives while ensuring brand consistency across high-volume materials. Collaborate with teams, defend design decisions, incorporate feedback, and deliver quickly. Preferred skills include writing, editing, animation, video, Premiere Pro, and After Effects.

Own end-to-end infrastructure delivery for strategic physical AI customers, from discovery and architecture through production rollout. Build cloud compute orchestration, platform services, onboarding environments, observability, security, and cost controls for simulation, training, inference, and batch workloads. Debug across infrastructure layers, productize recurring customer needs, and shape the physical AI platform roadmap. Partner with customers, Field CTO, Product, and Engineering while using AI coding tools to accelerate production engineering.

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.

Develop and maintain API-based integrations and cloud-native Azure services. Build workflow automation, identity lifecycle processes, data synchronization, CI/CD pipelines, and Terraform infrastructure. Troubleshoot APIs, Azure services, and data sources while documenting integrations and scripts. The role requires Python or C#, Azure integration expertise, secure authentication knowledge, relational database experience, and familiarity with deployment automation.

Drive cloud capacity expansion across data centers by analyzing resource consumption, forecasting demand, identifying risks, and translating forecasts into infrastructure requirements. Coordinate procurement, hardware delivery, installation, deployment, and customer availability across supply chain, data center, engineering, product, and business teams. Maintain expansion plans, communicate timelines and dependencies, and proactively resolve cross-functional issues.

Own and evolve the People Systems roadmap, partnering with HR stakeholders and the HiBob vendor on system enhancements, documentation, automation, self-service, and AI-driven support. Coordinate HRIS activities for mergers and acquisitions, improve data and lifecycle processes, and support analytics, reporting, and continuous improvement. Serve as deputy to the People Systems Manager while collaborating with technical and non-technical stakeholders in a global environment.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

Lead connectivity lifecycle for data center and PoP sites: assess site feasibility, design fiber pathways and POE/MMR layouts, manage carrier relationships and commercial negotiations (IRUs, leases), maintain geospatial/fiber documentation (KMZ/KML), and coordinate cross-functional delivery with procurement, construction and engineering teams across the region.

Lead and coordinate complex, cross-functional compute hardware and software programs (kernel, virtualization, drivers, firmware, GPUs). Manage dependencies, risks, execution, deployment automation, validation, observability, and customer/vendor interactions to scale large GPU/HPC cloud infrastructure.

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Design, implement, and operate the Virtual Private Cloud control and data planes. Improve performance, reliability, automation, and cross-service interactions. Develop networking features, perform load testing, and write high-performance code for cloud networking services.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.