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NVIDIA
NVIDIA
H1B vīzas sponsorsMākslīgais intelektsRažošanaVeselības aprūpe
NVIDIA izstrādā paātrinātās skaitļošanas un mākslīgā intelekta tehnoloģijas, ko izmanto spēļu, datu centru, mākoņdatošanas, veselības aprūpes, ražošanas, autobūves un robotikas jomās. Uzņēmuma darbība aptver grafikas procesorus, mākslīgā intelekta platformas, simulāciju un nozarei paredzētus risinājumus, tostarp NVIDIA Omniverse sadarbības 3D darbplūsmām, NVIDIA DRIVE autonomo transportlīdzekļu izstrādei un NVIDIA Clara veselības aprūpes lietojumprogrammām. Uzņēmums apvieno lielas tehniskās komandas, kas strādā pie infrastruktūras un programmatūras, kura nodrošina progresīvu skaitļošanu, simulāciju un mākslīgā intelekta vadītu analītiku.
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Vakances uzņēmumā

NVIDIA Senior AI and HPC Infrastructure Solutions Architect, Texas Remote

Design GPU clusters and accelerated infrastructure for NVIDIA energy-sector customers as a senior AI solutions architect. Advise on deployments spanning AI, high-performance computing, cloud, and edge environments.

USAmerikas Savienotās ValstisTXTeksasa
184 000 $ – 356 500 $ / gads
AttālinātiPilna slodzeVecākais speciālistsNepieciešamā Valoda: angļuRisinājumu inženieris
DockerKubernetesMākonisNode.jsSadalītās sistēmas

Apraksts

  • Work with energy customers, software vendors, and account teams on discovery, architecture reviews, technical evaluations, proofs of concept, benchmarks, demos, and production deployment advice.
  • Guide the design, deployment, and optimization of accelerated AI infrastructure, including large GPU clusters, AI factories, and edge systems.
  • Help design infrastructure across compute, networking, storage, containers, observability, security, power, and data center operations.
  • Promote monitoring, telemetry, and management tools that improve cluster utilization, reliability, performance, and workload visibility.
  • Share findings and help others learn through examples, white papers, blogs, and collaborative customer problem-solving.
  • Create reference architectures, deployment and sizing guides, benchmark reports, technical playbooks, demos, and white papers.
  • Build strategic relationships with key customers and industry-focused solution partners.
  • Work primarily with colleagues and customers through virtual collaboration tools.
  • Travel of up to 20% may be required for customer meetings.

Prasības

  • Bachelor’s, master’s, or doctoral degree in computer science, electrical or computer engineering, physics, another engineering discipline, or a related field; equivalent experience is also accepted.
  • At least eight years of hands-on work in AI infrastructure, accelerated computing, distributed systems, cloud infrastructure, high-performance computing, or machine learning platforms.
  • Demonstrated ability to design, deploy, and operate accelerated computing infrastructure at scale.
  • Detailed understanding of orchestration, scheduling, automation, and continuous integration and deployment pipelines for AI clusters.
  • Knowledge of data center networking, including InfiniBand, Ethernet, RDMA, network configuration, and performance tuning.
  • Familiarity with distributed training, inference serving, model deployment, storage performance, and cluster reliability needs.
  • Working knowledge of at least one energy sector and its operational and IT environments.
  • Experience designing AI factories, large GPU clusters, multi-node training environments, or production inference platforms.
  • Experience deploying large-scale infrastructure workflows for LLM training, fine-tuning, retrieval-augmented generation, and inference.
  • Experience measuring cluster performance with MLPerf, HPL, or workload-specific tests.
  • Application and systems-level knowledge of OpenMPI, NCCL, distributed training frameworks, and GPU communication patterns.
  • Comfort using Docker or other containers and Kubernetes.
  • Strong presentation, communication, problem-solving, documentation, and collaboration abilities.
  • Able to structure complex problems and work independently to deliver results on ambiguous projects with multiple stakeholders.

Priekšrocības

  • Equity compensation
  • Employee benefits

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