Cardiovascular Interventional Technologist Level II providing radiographic technical expertise during cardiovascular, neurovascular and angiographic procedures.
Compensation
Base Pay Scale: Generally starting at $76.29 - $85.91 per hour
Responsibilities
Sets exposure parameters and provides radiographic technical expertise during procedures
Prepares and assists with equipment such as balloons, stents, valves
Enters patient data into imaging equipment
Archives images in Cardiac PACS and Radiology PACS
The Scheduling team focuses on owning and extending systems that orchestrate compute allocation for complex machine learning workloads on Kubernetes.
Responsibilities
Own the Scheduling Layer: Design, operate, and extend ML workload scheduling systems to handle dynamic batch inference and long-running training workloads.
Optimize Compute Utilization: Drive the implementation of scheduling policies like gang scheduling, topology-aware placement, and preemption to ensure efficient use across our fleets of committed GPUs and TPUs.
Partner with Research: Collaborate directly with ML researchers and the broader Infrastructure team to understand workload behavior, troubleshoot bottlenecks, and turn research requirements into platform capabilities.
Ensure Reliability: Build systems with clear failure semantics. Participate in the operational lifecycle of the scheduling ecosystem, designing robust processes, runbooks, and SLOs to maintain high availability.
Build Custom Infrastructure: Build and maintain custom Kubernetes controllers, operators, and CRDs that adapt the cluster orchestration layer to the specific requirements of our ML pipelines.
Ingénieur·e DevOps GPU pour une mission stratégique à Guyancourt, spécialisé dans l’industrialisation et l’exploitation de plateformes GPUaaS et LLMaaS On-Premise.
Responsibilities
Industrialiser et automatiser le déploiement de plateformes IA dans des environnements On-Premise sécurisés.
Contribuer à l'évolution de l'architecture des plateformes GPUaaS et LLMaaS.
Participer à la mise en place de services managés en intégrant les contraintes de sécurité, de conformité et de gouvernance.
Assurer la mise en production, la documentation et le maintien en conditions opérationnelles et de sécurité des plateformes IA.
Participer à l'amélioration et à l'automatisation des processus de déploiement et d'exploitation.
Requirements
Formation supérieure en informatique, Bac+5 / M2 ou équivalent
Own strategy and roadmap for Private Cloud AI area across GPU compute, AI networking, and AI software stack, driving product launch and adoption.
Compensation
The expected salary/wage range for this position is provided below. Actual offer may vary from this range based upon geographic location, work experience, education/training, and/or skill level. – United States of America: Annual Salary USD 165,500 - 313,500 in Colorado // 178,000 - 339,000 in Massachusetts // 157,000 - 361,000 in Texas
Responsibilities
Own strategy and roadmap for Private Cloud AI area
Define platform layers including GPU, networking, storage, Kubernetes orchestration
Drive features to increase GPU utilization
Lead agentic AI roadmap
Partner with NVIDIA and open-source ecosystem
Requirements
Bachelor's degree in Computer Science, Engineering, or related field, or equivalent experience
Nice to have: AI frameworks and MLOps: LangChain, LangFlow, MLflow, Ray, SGLang
Nice to have: GPU scheduling: NVIDIA KAI Scheduler, Run:ai, Kueue, Volcano, Slurm
Nice to have: Agent runtimes: NVIDIA OpenShell, NemoClaw, OpenClaw, Hermes
Nice to have: NVIDIA AI Enterprise: NIM microservices, NeMo, Triton/Dynamo
Build and maintain the scheduling layer for GPU clusters across Kubernetes and SLURM, enabling shared capacity for training, inference, and batch workloads.
Locations (2)
London, England, United Kingdom
Helsinki, Uusimaa, Finland
Compensation
Cash and equity compensation along with local benefits.
Responsibilities
Design and build the scheduling layer that lets training, inference and batch workloads share GPU capacity across Kubernetes and SLURM
Build support for perishable compute and reclaim it when higher-priority work needs it
Implement gang scheduling, topology-aware placement and preemption for multi-node training with Volcano, KAI Scheduler, Kueue or Slurm Bridge
Develop Kubernetes operators and controllers for quotas, priorities, fair-share and queueing, and for the cluster lifecycle
Solve interactions between workload types such as elastic training and latency-sensitive inference
Own strategy and roadmap for your Private Cloud AI area across GPU compute, AI networking, and the AI software stack, tied to clear revenue, adoption, and utilization goals.
Compensation
The expected salary/wage range for this position is provided below. Actual offer may vary from this range based upon geographic location, work experience, education/training, and/or skill level. – United States of America: Annual Salary USD 165,500 - 313,500 in Colorado // 178,000 - 339,000 in Massachusetts // 157,000 - 361,000 in Texas The listed salary range reflects base salary. Variable incentives may also be offered.
Responsibilities
Own strategy and roadmap for your Private Cloud AI area across GPU compute, AI networking, and the AI software stack, tied to clear revenue, adoption, and utilization goals.
Define the platform across layers: GPU and accelerator configurations, Spectrum-X and InfiniBand fabrics, storage, Kubernetes orchestration, scheduling, and the curated AI tools customers deploy on day one.
Make GPUs pay off: drive the features that raise utilization and time-to-value, such as GPU sharing, fair-share quotas, gang scheduling, and multi-tenant isolation.
Lead the agentic AI roadmap: bring secure agent runtimes, model serving, and developer frameworks into the platform as supported, enterprise-ready experiences.
Partner deeply with NVIDIA and the open-source ecosystem to co-define integrations, validated designs, and release timing.
Requirements
Bachelor's degree in Computer Science, Engineering, or related field, or equivalent experience.
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