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NVIDIA

Designs the chips and software that power AI

Senior Site Reliability Engineer

Full-TimeUpdated on 10/15/2026
No salary listed
Senior, Expert
Bachelor's, Master's, PhD
Yokne'am Illit, Israel
In Person
DevOps & InfrastructureDatabase Administration

About the job

Requirements
  • A Bachelor of Science, Master of Science, or PhD in Computer Science, Engineering, or a related field, or equivalent experience.
  • At least 8 years of database engineering experience with deep expertise in database systems or distributed data platforms.
  • Hands-on expertise with one or more major relational database engines, such as Oracle, MySQL, or MSSQL, including replication topologies, backup and restore strategies, and high-availability architecture.
  • Experience in query optimization, data partitioning, and large-scale performance tuning.
  • Experience building or operating managed database services or internal Database-as-a-Service platforms, automating provisioning, monitoring, patching, and failover for database fleets at scale.
  • Strong programming skills in Python and Go, with experience building production-grade systems.
  • Experience crafting high-performance, high-availability relational database services.
  • Experience with container orchestration using Kubernetes and cloud-native database deployment patterns.
  • Hands-on experience establishing DevOps guidelines, including continuous integration and continuous delivery, monitoring, alerting, service-level agreements, and capacity forecasting.
Responsibilities
  • Design and operate highly available database clusters using MySQL, MSSQL, or Oracle, with automated replication, failover, point-in-time recovery, and enterprise-scale disaster-recovery strategies.
  • Drive database performance engineering by owning query optimization, indexing strategies, connection pooling, lock-contention analysis, and storage-engine tuning for production systems handling millions of transactions.
  • Build self-service database lifecycle automation, including one-click cluster provisioning, schema migrations, zero-downtime upgrades, blue-green deployments, and automated capacity scaling.
  • Extend traditional database expertise into vector search, GPU-accelerated query engines, and hybrid data platforms for conventional workloads and AI applications.
  • Compose and build software platforms that transform legacy database systems into modern, scalable architectures.
  • Run vector and graph database services and query engines for ultra-low-latency AI and machine-learning data workloads.
  • Build automation frameworks for provisioning, schema evolution, scaling, and failover, integrated into continuous integration and continuous delivery workflows.
  • Build developer-focused tooling to monitor, profile, and debug database performance in real time.
  • Contribute to architecture, coding standards, and guidelines for long-term platform evolution.
  • Participate in on-call rotations to ensure reliable operation of critical database services.
Desired Qualifications
  • Strong Kubernetes, infrastructure-as-code, and coding experience in addition to core database administration skills.
  • Expertise in hybrid or multi-region database replication strategies for low-latency AI workloads.
  • Understanding of observability and performance-profiling tools for complex data systems.
  • Experience building an internal Database-as-a-Service offering that lets engineers request a production-ready, monitored, and backed-up database cluster without filing a ticket.
  • Hands-on experience with database migration tooling, schema-evolution pipelines, and zero-downtime upgrade strategies across heterogeneous database engines.

About the company

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

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Simplify Jobs

Simplify's Take

What believers are saying

  • AI demand is still surging: data center sales rose 117% to $89 billion in the quarter ending July 2026.
  • Stock grants are worth more: shares hit a record in October 2026, up 28% for the year.
  • Engineering dominates hiring: 1,722 of NVIDIA's 2,686 job listings in October 2026 were in engineering.

What critics are saying

  • Big buyers are hedging: Meta's February 2026 agreement covers 6 gigawatts of AMD chips.
  • Financing its buyers is getting harder: lenders pushed back on NVIDIA's $500 billion chip-backed loan plan in October 2026.
  • Staff described long hours: Bloomberg's August 2024 interviews reported seven-day weeks, late nights and shouting matches.

What makes NVIDIA unique

  • Rivals must match its software too: over 7.5 million developers use CUDA and other NVIDIA tools to program its chips.
  • The CEO has a broad direct staff: Jensen Huang says over 60 people report to him, without one-on-ones.
  • People rarely leave: NVIDIA's yearly staff turnover is 3.7%, versus a 21.9% average across the tech industry.

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Benefits

Company Equity

401(k) Company Match

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

-1%

2 year growth

-2%
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Oct 15th, 2026
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Nvidia said Synopsys and Siemens are adopting its CUDA-X libraries for chipmaking. The announcement places Synopsys' adoption within a broader partnership with Nvidia on engineering and design. Nvidia said Siemens uses cuLitho for chip-pattern refinement and mask preparation. Siemens also works with Nvidia on industrial AI and manufacturing digital twins. Nvidia said cuPotential is available in early access. NanoVDB is open source within OpenVDB, while NPP is available through the CUDA Toolkit.

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Two weeks before ChatGPT launched, Nvidia's earnings call for the third quarter of fiscal 2023 held early signs of the generative AI boom. The November 16, 2022 report looked weak: revenue fell 17% year over year, gaming sales halved to $1.6 billion, and the company took a $702 million inventory charge tied to new US export rules on China. Yet data center revenue rose 31% to a record $3.8 billion, nearly two-thirds of the total. Management cited large language models and generative AI as growth drivers, and chief executive officer Jensen Huang called it "the beginning of the era of generative AI." Nvidia also began shipping its H100 data center chip that quarter. The stock was worth roughly $390 billion then, and $5.5 trillion in October 2026.

Yahoo Finance
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Nvidia shares fall 2.9% despite new 250kW AI rack designs

Nvidia is opening more of its Vera Rubin infrastructure designs to industry partners, though its shares fell 2.8% to $222.22 at 12:12 p.m. ET on October 14. The company's MGX 1.2 rack designs support up to 250 kilowatts. They include a liquid-cooled 5,000-amp busbar, standardised power and cooling connections, and support for 19-inch equipment. Nvidia's Vera CPU rack also handles orchestration and tool execution for AI agents. Separate grid-resilience specifications call for systems to recover to near-full load in under two seconds after disturbances. The figures describe design limits, not installed capacity or booked revenue. Nvidia's October 12 announcement gave no customer order value. Investors will watch for operating facilities and repeat purchases.

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Nvidia unveils cuPotential as chip leaders adopt CUDA-X for AI-defined fabs, with up to 30x speedups

NVIDIA announced on October 14, 2026 that semiconductor leaders are adopting its CUDA-X libraries to build AI-defined fabs, factories where AI helps engineers optimise processes and fix production issues. The news came at SEMICON West in San Francisco. The company introduced cuPotential, a library for building AI agents that simulate how atoms and molecules interact. In early-access testing, it sped up already GPU-optimised workflows by an additional 2-3x. Synopsys is among the early users. Lam Research expects NanoVDB, an open source 3D data library, to speed up some wafer-etch modelling by up to 30x. Applied Materials, Cadence, KLA, and Siemens are also using NVIDIA's tools across chip design, manufacturing, and testing.