Jobs at Nebius

Own end-to-end product for a slice of Nebius AI compute platform: define strategy, APIs, and roadmaps; drive cross-team delivery; conduct structured discovery; measure adoption and platform outcomes; engage engineering as a technical peer; and close operational feedback loops to improve reliability and customer experience.

Build and scale core observability services for high-volume telemetry ingestion, distributed storage, query engines, alerting pipelines, and AI-assisted troubleshooting. Work on reliability, performance, and production incident resolution across a cloud AI platform.

Advise business and product teams on privacy, data protection, and AI compliance across global jurisdictions. Conduct DPIAs, RoPAs, vendor and cross-border transfer assessments, draft privacy documentation and contractual clauses, support incident response and regulatory notifications, and collaborate with InfoSec, Risk, and Legal stakeholders to mitigate privacy risks.

As GTM Recruiting Manager, you'll lead recruitment across Sales, Marketing, Partnerships, and Customer functions, aligning hiring strategies with regional expansion and revenue targets in EMEA and APJ.

The Principal Solutions Architect will lead presales, design AI/ML infrastructures, mentor other architects, and drive customer adoption for Nebius AI cloud solutions.

Provide technical support for AI platform customers by triaging tickets, debugging production LLM applications, escalating to engineering, improving diagnostics and runbooks, participating in incident post-mortems, tracking support KPIs, and enhancing documentation to improve customer self-service and satisfaction.

The Network Site Reliability Engineer will define reliability goals, enhance network reliability, manage incident responses, build observability, and automate workflows.

Lead and build Nebius' Detection & Response capability: design detections, achieve MITRE coverage, run incident response and forensics, integrate threat intelligence, build D&R tooling and SOAR workflows, and manage cross-functional stakeholders and metrics (MTTD/MTTR). Hire, mentor, and scale a team and on-call program.

The Data Engineer will design and maintain scalable data pipelines, optimize data infrastructure, monitor data quality, and enable analytics capabilities, ensuring alignment with business goals.

Design, operate and troubleshoot large-scale data center and backbone networks including InfiniBand GPU cluster interconnects. Provide technical design and operational support, develop monitoring and automation tools, document network designs, test infrastructure and vendors, and support launch of new cloud regions.

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.

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.

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, 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.

Build and operate services and tooling that automate network lifecycle, make network changes safe (CI/CD, rollouts, audit trails), develop scalable observability and telemetry, integrate network source-of-truth with platform APIs, and collaborate with network engineers and SREs to turn operational pain into production-grade automation.

Design, deploy, and optimize global enterprise wired/wireless networks and VPNs. Lead connectivity between branches, automate operations and monitoring, troubleshoot cross-region incidents, evaluate new technologies, and partner with telecom providers to manage SLAs and escalations.

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.

Lead reliability and observability for compute nodes running VMs. Debug Linux user/kernel issues, troubleshoot CPU/memory/NUMA/cgroups, operate QEMU/KVM and container tech, design node-level metrics/logs/traces/SLIs/SLOs, run incident response and collaborate across platform, kernel, GPU and infrastructure teams.

Work on YDB distributed storage and database systems to maximize performance on modern and legacy hardware (NVMe, HDD, DPUs). Reengineer components for low-latency, high-throughput workloads, profile and debug production systems, and design resilient distributed storage primitives. Collaborate on incident resolution and production deployments to support Nebius cloud services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.