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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.

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.

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.