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Solutions Architect at a cloud platform company, advising enterprise AI customers on deploying and scaling GPU workloads for ML training and inference using Nebius’s AI cloud services.
Lead a team optimizing GPU cluster performance for an AI cloud platform, diagnosing bottlenecks across hardware, networking, and software stacks like InfiniBand, KVM/QEMU, and NCCL.
Partner Solutions Architect designs and implements integrations between Nebius' AI cloud platform and strategic partners, enabling joint customers to deploy GPU-accelerated AI/ML solutions at scale.
Designs and implements embedded firmware for GPU and HPC platforms, focusing on board management, telemetry, and hardware-firmware integration in high-density, mission-critical environments.
Senior engineer builds and optimizes GPU clusters, InfiniBand networks, and KVM/QEMU virtualization for a cloud platform powering AI workloads.
Senior Technical Program Manager at Nebius leads end-to-end delivery of GPU clusters and AI infrastructure, coordinating engineering, networking, construction, and operations teams to launch hyperscale data centers globally.
Designs and advises on cloud infrastructure and MLOps solutions for AI/ML teams, leveraging GPU cloud platforms and modern frameworks to optimize training and inference workloads.
Own the product direction for Soperator, Nebius's Slurm-on-Kubernetes control plane for GPU clusters, shaping how ML engineers run and scale distributed AI workloads using cloud infrastructure and orchestration tools.
Technical Product Manager building an AI cloud platform that supports training and inference at scale, focusing on GPU orchestration, reliability, and developer experience.
Owns the operational health and SLA compliance of Nebius' global data center fleet, coordinating maintenance, audits, and vendor relationships to ensure reliable AI cloud infrastructure.
Owns the storage product vision and roadmap for a cloud AI platform, defining technical requirements for block, file, object, and HPC storage while balancing performance, durability, and cost.
Senior engineer embedded with customers to design, build, and deploy cloud infrastructure that runs physical AI workloads like simulation, training, and inference on Nebius's AI cloud platform.
Designs and optimizes large-scale GPU clusters for AI workloads, focusing on compute, networking, storage, and reliability across data centers.
Designs and implements cloud infrastructure and MLOps solutions for AI teams, advising on GPU orchestration, Kubernetes, and IaC tools like Terraform.
The Principal Solutions Architect will lead technical presales and solution design for strategic customers in Japan, focusing on large-scale AI/ML infrastructure and GPU-accelerated cloud workloads. The role involves acting as a trusted advisor to executive stakeholders and collaborating with internal product and engineering teams to drive adoption of the Nebius AI cloud platform.
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Senior engineer builds and maintains open-source AI infrastructure in Seattle, designing Go/Python systems to schedule and optimize GPU clusters for transparent, high-performance AI research.
Build and scale MLOps infrastructure for an AI-driven ad platform, designing Prefect-based pipelines for training and deploying recommendation models that power real-time bidding at massive scale.
Build and operate MLOps infrastructure for an AI-native ad-tech platform, focusing on scalable model training and deployment pipelines using Python, Spark, and Prefect to support real-time bidding systems.
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