Taro Events
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Generative AI, specifically large language models, have taken the world by storm. But they wouldn't exist today without a key paper published by Google Brain: "Attention Is All You Need." The 10-page paper, published in 2017, introduced the Transformer - a critical innovation in neural networks.
What's in store?
- A deep dive and interactive discussion of the paper. We recommend reading (or at least skimming) it first:Â https://arxiv.org/pdf/1706.03762.pdf
- A look at what came before the paper (neural networks, embeddings), and what came after (BERT, GPT-2, and ChatGPT).
- All software engineers welcome - no AI/ML background necessary!
Did you know?
- The paper's authors have all gone on to found multiple billion-dollar AI companies including Cohere and Character.ai.
- Since 2017, the paper has been cited over 86,000 times.
- The Transformer architecture is the T in "GPT".
Your host:
Charlie Guo is a seasoned founder and author with over 15 years of software experience and more than a decade in Silicon Valley. He is an alum of both Stanford and YCombinator, and currently publishes Artificial Ignorance, an AI-focused newsletter for engineers and founders.
The agenda:
- Charlie will give a brief background, and then explain the key insights and context from the paper.
- We will open the group to some Q&A and a broader discussion.
- And we'll end with some discussion on which paper to read and review next.
The next paper reading group is on October 15 about deep reinforcement learning, led by Sai Bhavanasi.
In 2021, the last full year Rahul worked at Meta, he made $823K. When he joined Facebook (Meta) in 2017, his total compensation was $390K. See the full details in the YouTube video.
In this session, Rahul will document how his yearly compensation more than doubled over the 4.5 years he was employed at the company. This talk will cover details that didn't fit into the YouTube video while exploring angles of it more in-depth.
Topics:
- What components of compensation matter the most in a typical tech company
- Onboarding effectively and overcoming anxiety around sky-high expectations (which will come with starting at $400k TC)
- Finding Staff Engineer scope and working with your manager on defining (and then following through on) a promotion plan
- How Rahul was able to get Additional Equity (AE) with a stellar project, greatly boosting his compensation
- The psychology around pay changes
We recommend going through these before joining:
This is a resume meetup to collaboratively review resumes in small groups.
Here's how it works:
- People take turns sharing their resume in the call as a presentation.
- People give feedback on the resume (5-7 minutes).
- We will try to get through everyone's resume. We plan to do it on a first-come, first-serve basis for fairness
Here's the masterclass on resume writing for reference: [Masterclass] How To Write A Stellar Tech Resume That Gets You More Job Opportunities
If we aren't able to go through all the resumes, follow up in the #job-searchers-accountability-group channel.
Here's some additional resume optimization resources:
Topic: Chapter 7 - Transactions
In this #taro-bookclub discussion, we'll go over Chapter 7 of "Designing Data-Intensive Applications" by Martin Kleppmann.
Here's the agenda:
- We’ll break down and discuss the key concepts from Chapter 7.
- There will also be time for any questions and answers you might have.
Please send your questions ahead of time to Ankur Agrawal in the Taro Premium Slack, so that they can be included in the discussion.
Get ready for another engaging and informative session!
Edbert Chan is a senior Android engineer at Uber. He wrote the popular guide “Welcome To The Coding Interview - You Suck” freely available here.
The extensive guide is filled with practical tips for job seekers to pass the modern interview process at tech companies. In this session, Edbert will cover the 9 mistakes that hold people back from passing technical interviews, including:
- Not Putting Enough Time
- Interview Approach Is Inconsistent
- Not Fixing Your Own Mistakes
- Not Understanding Your Own Solution Before Coding
- Not Being Fast Enough
- Being Full of Shit
Per Edbert's request, consider donating to SFSPCA, a nonprofit animal shelter in San Francisco!
Additional resources:
Google's Deep Mind paper Human-level control through deep reinforcement learning was the catalyst in Reinforcement Learning in 2015. Advances from this paper today are used in ChatGPT, robots, video game characters, ad ranking, and much more. The paper has over 25k citations and is published in Nature.
What's in store?
- Discussion on what Reinforcement Learning is
- A discussion of the paper and the Deep Q-Network model discussed in the paper
- Look at how Reinforcement Learning is applied to various domains
You can download the paper from Google Deep Mind's website
Your host:
Sai Shreyas Bhavanasi has published 2 first author papers applying Deep Reinforcement Learning to path planning and is currently working in computer vision research building Image Retrieval models for sparse data applications.
RSVP for this event on LinkedIn!
While on the job hunt in 2019, Steven Zhang received 18 offers (out of 22 onsites), including from companies such as Airbnb, Apple, Facebook, Lyft, Palantir, Pinterest, Stripe, Uber.
In the process, he unintentionally became an expert in the software engineer interview and job search. In this talk, Steven will share his process to determine where to apply, how he prepared, and how he applied to so many companies.
He documented some of this already in his viral Medium article.
Steven has worked for Airtable, the U.S. Digital Response, and Tableau. He also has a deep interest in climate tech and runs the web's largest climate tech jobs aggregator ClimateTechList.com. Connect with Steven on LinkedIn
![[Masterclass] How To Work Better With Your Engineering Manager event](/_next/image/?url=https%3A%2F%2Ffirebasestorage.googleapis.com%2Fv0%2Fb%2Ftech-career-growth.appspot.com%2Fo%2Fevent_images%252F1693218141-image.jpg%3Falt%3Dmedia%26token%3Ded119d17-7aab-4704-9a26-49ca7080ba23&w=3840&q=75)
Bay Area Tarodactyls unite!
Join us in Palo Alto for the Silicon Valley Taro meetup! This will be our biggest and greatest software engineer meetup to date. Come by to:
- Network with 80+ talented and ambitious tech folks. We'll segment folks based on technology and hiring needs.
- Hear a keynote from a distinguished engineering leader (TBD).
- Enjoy snacks and drinks (yes, we will have Taro boba!)
- Meet Alex, Rahul, and other industry veterans from companies like Meta, Robinhood, Google, and Mixpanel.
To stay up-to-date on logistics (especially on the day-of), make sure you're a part of the #g-sf-bayarea-meetup channel in the Taro Premium Slack.
We're excited to meet you! Thanks to StartX for their support in the event.
Nikita Gupta worked as a Senior Technical Recruiter at Uber and Amazon with 3+ years of recruiting and hiring experience. She extensively used applicant tracking systems (ATS) to search for the right candidates and hire them.
Nikita is an expert in helping candidates create better resumes and LinkedIn profiles in order to land more job opportunities. Her company CareerFlow.ai does exactly this.
As a full-cycle recruiter with experience in Big Tech and startups, Nikita will talk about how FAANG companies hire, and what misconceptions engineers often have when applying to jobs. She'll cover:
- Does "keyword stuffing" in resumes increase the chance of getting an interview?
- What are the top 3 things a recruiter looks for when opening a resume?
- How much does the recruiter screen matter in the overall interview process?
- How much does your LinkedIn matter in the job search?
Discounts for CareerFlow.ai's resume review service are available from the perks page.
Additional resources:
