Senior Network Site Reliability Engineer
About the job
- Educational Background: Bachelor's degree in Computer Science, Electrical Engineering, or a related technical field, or equivalent experience.
- Work Background: A minimum of 10 years of industry practice in network operations or related fields concentrating on support, automation & site reliability engineering. Familiarity with both enterprise and the data center networks is critical.
- Strong proficiency in network fundamentals & fixing complex network issues with expertise in network tech like TCP/UDP, IPv4/IPv6, BGP, OSPF, ISIS, VPN, L2 switching, Firewalls, Load Balancers, Data Center Network technologies, Wireless etc. Consistent track record in network operations.
- Monitoring Tools: Familiarity with network management tools such as Prometheus, Grafana, Alert Manager, Nautobot/Netbox, BigPanda.
- Network Automation: Experience in automating networks using frameworks such as Salt, Ansible, Python or similar.
- Process & Service Tooling: Skills with ServiceNow, Jira & foundational knowledge of ITIL framework
- System Administration: Knowledge of Linux system fundamentals.
- Problem-Solving and Communication: Detailed problem-solving approach, critical thinking, coupled with good interpersonal skills and a solid grasp of ownership and drive.
- Owning the operational aspect of the network infrastructure, ensuring its high availability and reliability, actively working on network incidents and service requests.
- Partnering with architecture and deployment teams to guarantee that new implementations are supportable and align with production standards.
- Advocating for and implementing automation to reduce toil and improve operational efficiency. Minimizing manual operational tasks to achieve and maintain Service Level Objectives (SLOs).
- Monitoring network performance, identifying areas for improvement, and collaborating with relevant teams to implement refinements. Proactively identifying and mitigating network risks to promote continuous improvement.
- Collaborating with domain experts across functions to resolve production issues swiftly and effectively, ensuring customer happiness.
- Conducting blameless postmortems and following through on Root Cause Analyses (RCAs).
- Discovering opportunities for operational improvements and teaming up with colleagues to devise solutions that enhance excellence and sustainability in network operations.
- Developing knowledge base articles for automation and bots.
- Support Automation: Track record of taking operational signals through means such as SNMP, Syslog, Streaming Telemetry to solve operational challenges.
- Platform Exposure: Experience with Mellanox/Cumulus Linux, Cisco/ Arista, Palo Alto firewalls, Versa SDWAN, Netscalers and F5 load balancers.
- Technical Proficiency in Programming and Scripting: Competence in Python, Go, or related programming languages. Skilled in constructing intricate systems to supervise and regulate network operations, surpassing elementary script development.
- Network Technologies: Proficient knowledge of one or more of the following technologies: VXLAN/EVPN at Scale, MPLS, RSVP, Segment Routing, SDWAN, SASE Platforms
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'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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-2%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.
Nvidia CEO Jensen Huang announced investments in five UK startups at a London event, backing Boltz Bio, Foresight Works, Hivergé AI, PrimaMente, and quantum computing firm Riverlane — extending Nvidia's AI-ecosystem strategy across Europe's deep-tech capital.
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.
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.
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.