NVIDIA
Designs GPUs and AI HPC platforms
GPU Performance Software Engineer – New College Grad 2023
About the job
- Pursuing BS, MS, or PhD in Computer Science or Software Engineering or equivalent experience
- At least one year of experience with C and C++ development
- Familiar with source code control software such as Perforce or GIT
- Experienced with software development practices such as design and code reviews
- Comfortable developing software in the Windows 10 or Windows 11 environment
- Development of low-level systems software and experience with scripting tools such as Python or Perl
- Fundamentals of statistics, including variance analysis
- Developing software on other operating systems, such as Linux or OSX
- The use of graphics APIs such as OpenGL or Direct3D
- GPU compute frameworks such as CUDA or OpenCL
- Machine Learning frameworks such as PyTorch or TensorFlow
About the company
NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.
Company Size
10,001+
Company Stage
IPO
Headquarters
Santa Clara, California
Founded
1993
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Simplify's Take
What believers are saying
- September 9, 2026 Australia partners target a 2-gigawatt NVIDIA AI buildout by 2027.
- September 21, 2026 Google, Emerald AI, and NVIDIA launched flexible data-center management.
- August 2026 revenue reached $96.2 billion, with data center sales at $89 billion.
What critics are saying
- Apple's September 2026 Mac Studio demo pushes stable inference off rented GPU clouds.
- U.S. export controls tightened May 31, 2026, blocking Nvidia chips from Chinese subsidiaries abroad.
- Power, cooling, and grid limits delay DSX builds; customer-owned clusters slash recurring GPU rentals.
What makes NVIDIA unique
- September 21, 2026 DSX Ready makes NVIDIA the reference layer for AI factory power and cooling.
- Vera Rubin NVL72 still anchors NVIDIA's fastest inference platform, shown September 16, 2026.
- NVIDIA now orchestrates chips, networking, software, and infrastructure partners across AI campuses.
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Growth & Insights and Company News
6 month growth
↑ 0%1 year growth
↓ -1%2 year growth
↓ -2%Former OpenAI data centre chief Chris Malone has joined Nvidia as vice president of the DSX Platform, according to his LinkedIn profile. The DSX division helps customers design and build AI data centres according to Nvidia's specifications. Malone previously led data centre operations at OpenAI. His move to Nvidia comes as the chipmaker expands its infrastructure services business to support the growing demand for AI computing facilities. The appointment strengthens Nvidia's team focused on large-scale AI infrastructure deployment.
AMD surpassed $1 trillion in market value on 21 September, becoming the fourth US chipmaker to reach that threshold. The stock has surged 187% this year, driven by AI demand for chips. Nvidia, five times larger than AMD, has gained 22% in 2026 but hasn't reached a new record since mid-May. The company reported second-quarter revenue of $96.2 billion, up 106% year over year, with net income rising 126% to $59.7 billion. Nvidia's CFO Colette Kress guided for approximately 70% revenue growth in fiscal 2028, calling it a "supply-constrained outlook". At Monday's close of $227.38, shares trade at roughly 15 times expected fiscal 2028 earnings. AMD's second-quarter revenue reached $11.5 billion, up 50% year over year.
Nvidia shares fell nearly 1.3% to $227.98 on Wednesday after Apple demonstrated four connected Mac Studios running a trillion-parameter AI model to fix a coding bug. Apple's pitch targets businesses seeking to avoid recurring cloud costs by purchasing machines outright for steady workloads or sensitive data processing. The challenge comes as Nvidia's data centre business dominates its revenue, generating $89 billion of $96.2 billion total revenue, or roughly 92.5%, in its latest quarter. Whilst Apple's desktop approach is unlikely to replace large systems used for training frontier models, investors now face a narrower question: how much routine AI work can shift to customer-owned machines before requiring cloud GPU resources?
Nvidia launched its DSX Ready program on 21 September, qualifying Tesla and Vertiv as suppliers for AI data centre infrastructure. The initiative certifies battery-energy-storage and cooling equipment compatible with Nvidia's high-density GPU systems. Tesla's Megapack battery system received qualification, positioning the company to supply power backup and grid-support solutions for AI facilities. This opens a new revenue stream beyond utilities and renewable projects. Vertiv secured approval for its 2.3-megawatt CoolChip coolant distribution unit. Higher rack density increases cooling requirements per megawatt, potentially accelerating Vertiv's growth beyond overall data centre expansion. The programme aims to standardise components around Nvidia's GPU infrastructure, though the company notes qualification does not guarantee project wins or replace site-level engineering. Hedge fund data from Q2 showed 285 funds holding Nvidia, 116 holding Tesla, and 112 holding Vertiv.
IonQ will deploy its Superion 256 quantum computer at NVIDIA's Accelerated Quantum Research Centre, marking the first on-premise quantum computing installation at the facility. The system will connect directly with an NVIDIA GB200 NVL72 system via NVIDIA NVQLink, with workloads managed by the CUDA-Q platform. The joint research programme will focus on hybrid software development and large-scale system prototyping. IonQ's roadmap targets 10,000 qubits and full fault tolerance through vertical integration of its SkyWater CMOS foundry. Earlier this month, the companies published joint research with Oak Ridge National Laboratory and the University of Tennessee, introducing a framework combining generative AI with distributed quantum algorithms for complex optimisation challenges. The partnership aims to advance the integration of quantum processors with supercomputing hardware.