• Tech giants are known for their challenging interviews.
• Google, Meta, and Nvidia top the list of rigorous interviews with multiple rounds and assessments.
• But tough questions show up across industries, according to employee reports on Glassdoor.

It's tough to break into high-paying companies.

Google is notorious for having a demanding interview process. Aside from putting job candidates through assessments, preliminary phone calls, and asking them to complete projects, the company also screens candidates through multiple rounds of interviews.

Typical interview questions range from open-ended behavioral ones like "tell me about a time that you went against the status quo" or "what does being 'Googley' mean to you?" to more technical ones.

At Nvidia, the chipmaking darling of the AI boom, candidates must also pass through rigorous rounds of assessments and interviews. "How would you describe __ technology to a non-technical person?" was a question a candidate interviewing for a job as a senior solutions architect shared on the career site Glassdoor last month. The candidate noted that they didn't receive an offer.

Tech giants top Glassdoor's list of the hardest companies to interview with. But tough questions show up across industries — from luxury carmakers like Rolls-Royce, where a candidate said they were asked to define "a single crystal," to Bacardi, where a market manager who cited a difficult interview, and no offer, recalled being asked, "If you were a cocktail what would you be and why?"

The digital PR agency Reboot Online analyzed Glassdoor data to determine which companies have the most challenging job interviews. They focused on "reputable companies" listed in the top 100 of Forbes' World's Best Employers list and examined 313,000 employee reviews on Glassdoor. For each company, they looked at the average interview difficulty rating as reported on Glassdoor.

Here's a list of the top 90 companies that put candidates through the ringer for a job, according to self-reported reviews on Glassdoor.

• Amazon addressed GPU shortages with internal tenets to guide the valuable resource's allocation.
• The company launched "Project Greenland" to streamline GPU distribution and prioritize ROI.
• Amazon employees now have better access to GPUs, the company said.

Amazon, like many other tech companies, has grappled with significant GPU shortages in recent years.

To address the problem, it created eight "tenets," or guiding principles, for approving employee graphics processing unit requests, according to an internal document seen by Business Insider.

These tenets are part of a broader effort to streamline Amazon's internal GPU distribution process. Last year, Amazon launched "Project Greenland," which one document called a "centralized GPU orchestration platform," to more efficiently allocate capacity across the company. It also pushed for tighter controls by prioritizing return on investment for each AI chip.

As a result, Amazon is no longer facing a GPU crunch, which strained the company last year.

"Amazon has ample GPU capacity to continue innovating for our retail business and other customers across the company," an Amazon spokesperson told BI. "AWS recognized early on that generative AI innovations are fueling rapid adoption of cloud computing services for all our customers, including Amazon, and we quickly evaluated our customers' growing GPU needs and took steps to deliver the capacity they need to drive innovation."

How Amazon decides who gets GPUs

Here are the eight tenets for GPU allocation, according to the internal Amazon document:

1. ROI + High Judgment thinking is required for GPU usage prioritization. GPUs are too valuable to be given out on a first-come, first-served basis. Instead, distribution should be determined based on ROI layered with common sense considerations, and provide for the long-term growth of the Company's free cash flow. Distribution can happen in bespoke infrastructure or in hours of a sharing/pooling tool.
2. Continuously learn, assess, and improve: We solicit new ideas based on continuous review and are willing to improve our approach as we learn more.
3. Avoid silo decisions: Avoid making decisions in isolation; instead, centralize the tracking of GPUs and GPU related initiatives in one place.
4. Time is critical: Scalable tooling is a key to moving fast when making distribution decisions which, in turn, allows more time for innovation and learning from our experiences.
5. Efficiency feeds innovation: Efficiency paves the way for innovation by encouraging optimal resource utilization, fostering collaboration and resource sharing.
6. Embrace risk in the pursuit of innovation: Acceptable level of risk tolerance will allow to embrace the idea of 'failing fast' and maintain an environment conducive to Research and Development.
7. Transparency and confidentiality: We encourage transparency around the GPU allocation methodology through education and updates on the wiki's while applying confidentiality around sensitive information on R&D and ROI sharable with only limited stakeholders. We celebrate wins and share lessons learned broadly.
8. GPUs previously given to fleets may be recalled if other initiatives show more value. Having a GPU doesn't mean you'll get to keep it.

Do you work at Amazon? Got a tip? Contact this reporter via email at [email protected] or Signal, Telegram, or WhatsApp at 650-942-3061. Use a personal email address and a nonwork device; here's our guide to sharing information securely.

In the wake of the Switch 2 reveal, neither Nintendo nor Nvidia has gone into any detail at all about the exact chip inside the upcoming handheld—technically, we are still not sure what Arm CPU architecture or what GPU architecture it uses, how much RAM we can expect it to have, how fast that memory will be, or exactly how many graphics cores we're looking at.

But interviews with Nintendo executives and a blog post from Nvidia did at least confirm several of the new chip's capabilities. The "custom Nvidia processor" has a GPU "with dedicated [Ray-Tracing] Cores and Tensor Cores for stunning visuals and AI-driven enhancements," writes Nvidia Software Engineering VP Muni Anda.

This means that, as rumored, the Switch 2 will support Nvidia's Deep Learning Super Sampling (DLSS) upscaling technology, which helps to upscale a lower-resolution image into a higher-resolution image with less of a performance impact than native rendering and less loss of quality than traditional upscaling methods. For the Switch games that can render at 4K or at 120 FPS 1080p, DLSS will likely be responsible for making it possible.

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