Interesting Data Gigs # 35: Get a job at NVIDIA ASAP

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Hello, Data Geek.

Today I want to give you some quick advice here: Use a “pick and shovel approach” to your job search today.

I know you should be wondering what I mean by that, so let me explain it.

A “pick and shovel approach” to job search is very simple:

Of course, there are some exceptions to this rule, especially after the recent layoffs at Amazon Web Services, Meta, IBM’s Red Hat, and many others.

Many companies are pivoting their services to AI in some form, and I firmly believe that the best company positioned to grow exponentially from today to the future is one and one only: NVIDIA.

If you read any news related to AI, all paths are redirected to Nvidia in some way.

And if you read about every AI partnership, Nvidia is present in some form:

• NVIDIA and Google Cloud Deliver Powerful New Generative AI Platform, Built on the New L4 GPU and Vertex AI

• AWS and NVIDIA Collaborate on Next-Generation Infrastructure for Training Large Machine Learning Models and Building Generative AI Applications

• Adobe and NVIDIA Partner to Unlock the Power of Generative AI

• Ampere, NVIDIA Extend AICAN Gaming Platform Ecosystem

• Shutterstock Teams With NVIDIA to Build AI Foundation Models for Generative 3D Artist Tools

And this is just the beginning. The potential is there.

So: GET A JOB AT NVIDIA ASAP IF YOU HAVE THE CHANCE.

First things, first: Why NVIDIA, why now? 3 Things to do

It’s just a matter of perfect timing, but Jensen Huang and his team at NVIDIA have worked on this for more than 20 years in the making.

They have taken the right steps to be the leader they are today.

I don’t want to tell the whole story about NVIDIA because there are better resources for that.

So, I will give you three core recommendations here.

First, you must listen to/watch the NVIDIA-focused episode in the Acquired FM podcast, hosted by Ben Gilbert and David Rosenthal.

These guys made an outstanding job explaining why NVIDIA is a leader in Accelerated Computing as Jensen calls it.

So, my first recommendation if you want to be part of the company, listen to or watch this episode and take notes about it:

The second recommendation is to listen to/watch this recent conversation between Elad Gil, Sarah Guo, and Jensen Huang.

Jensen gave a lot of tips there, and you feel listening to this conversation that he knows not only his company in and out, but he is an industry student as well, and I strongly believe this is a fundamental skill inside NVIDIA.

Why?

Because the company works with so many different organizations from a large scope of different challenging fields like LLMs, protein engineering, life sciences, and more.

Watch it on YouTube, or listen to the podcast on Apple Podcasts or Spotify 

And the third recommendation but not less important is to watch the keynote from the past GTC 2023 conference made by Jensen.

It’s packed with information, announcements, strategic alliances, and more. If you are working in the AI field, you need to watch this keynote. Seriously. Even if you are not a prospective employee of NVIDIA.

Which role?

If you ask me about it, I would pick a role inside the NeMo LLM Service.

There are two interesting open roles there:

• Site Reliability Engineering Manager - NeMo LLM Service

• Senior Cloud Backend Engineer, NeMo LLM Service

Why inside this service? 

It’s very simple: this will become in a high-demand service for NVIDIA, and if you are part of this foundational team, this could be a career catapulting movement for you.

What do you need to know?

• NVIDIA NeMo Framework

• NVIDIA Picasso, a Cloud-Based Generative AI Service for Creating Images, Videos, and 3D Applications

  If you have the chance, do this lab called: Train a Large-Scale NLP Model with NeMo Megatron

• Watch this talk from Nirmalya De (Principal Product Manager- Conversational AI and Deep Learning at NVIDIA) called Efficient At-Scale Training and Deployment of Large Language Models

• From Ingestion to Deployment for Large Language Models

Getting a role inside NVIDIA won’t be easy but it’s not impossible if you put the effort into it.

I let you here some articles to prepare better for it:

• Life at NVIDIA

• Land Your Dream Job at NVIDIA, from Study Data Science

• NVIDIA’s Interview Questions on Glassdoor

Good luck with your job application

Job Search Resources of the Week

✅ Zach Wilson on what makes a great data engineer: A conversation with Benjamin Wagner on the Firebolt Data Engineering Show. Listen on Spotify or Apple Podcasts

🚨 Shreyas Doshi is opening the next cohort of his Career Management course for Product Managers

📜 Jean Kang shared on LinkedIn a lot of great tips about how to better prepare your resume for a Product Management/Program Management role.

BTW, if you are trying to get a job in those roles, definitely you must subscribe to her newsletter called “Path to PM”.

🌴 According to Marina Aisa, Apple is hiring front-end/full-stack engineers in San Diego and Barcelona

😬 Jordan Mazer (from a16z Games) shared a lot of practical tips about how to handle job offers rejections

🔲 Square is hiring Analytics roles remotely

#1 Data Career Week: Tips, Tools and Remote Data Jobs, by Sairaj  

Other beehiiv-based newsletters you should check out

Ben’s Bites

Openly’s Work in Sports

Interesting reads of the week

🛠️ Adopting a New DataOps Tool, by Sarah Floris

🤖 Apple Plans AI-Powered Health Coaching Service, Mood Tracker and iPad Health App

🤖 AutoGPT is huge, and this massive thread from Angry Tom probes it

📉 How we reduced a 6-hour runtime in Alteryx to 9 minutes with dbt and Snowflake, by Arthur Marcon, Lucas Bergo Dias, and Christian van Bellen from Indicium Tech

📈 Scaling Databases at Activision, by Greg Smith and Vladimir Kovacik from Demonware

🤖 How to train your own Large Language Models, by Reza Shabani from Replit

🤖 A very interesting perspective about what Machine Learning innovation actually means by Damien Benveniste 

🪳 Hasura & CockroachDB Easily Handle Thousands of Requests Per Second with Low CPU Utilization – Part 1

🤖 What Are Transformer Models and How Do They Work? by Luis Serrano from Cohere

🤖 Introducing Lamini, the LLM Engine for Rapidly Customizing Models, by Sharon Zhou

Final words

Thanks a lot for reading my stuff, and if you have any feedback about this new format, let me know.

Marcos out.

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