5 Offers w/ FAANG Companies As A New Grad: Details + Lessons

In 2014, Rahul graduated from Stanford with a BS and MS in Computer Science. He received new grad Software Engineer offers from 5 companies: 
1. Facebook
2. Twitter
3. Microsoft
4. Google
5. Square

This video outlines the exact details of the offers, and models out the increase (or decrease) in value of those offers 8 years later, in 2022. The total compensation of the offers range from $174K/year to $262K/year, and those numbers go a lot higher if you factor in stock appreciation.

If you want to better understand how compensation works in top tech companies, first watch [this video](https://link.jointaro.com/819tdsqwJj4VnaEC6).

Asking Great Questions [Part 13] - Please Don't Do This: Leading Hello

A lot of what we're taught growing up doesn't translate very well into the software engineering world, and how we greet people is one of those things. This lesson teaches you:

- Why a standard solitary "Hello" or "Hi" is a terrible way to get help digitally
- What being polite actually means in the engineering world
- What you should do instead of leading hello

If you want to join the cause of "No Hello": [https://nohello.net/](https://nohello.net/en/)

Engineering Management Made Easy: The ABCs for First-Time Managers

How to avoid burnout, enjoy being a manager, and multiply your team’s efforts.

I will share mistakes that I made when I first became an Engineering Manager and what I learned from those, followed by Q & A.

Connect with Umang on LinkedIn: [**https://www.linkedin.com/in/umangv/**](https://www.linkedin.com/in/umangv/)

Mid-Level Engineer (L4) Describes Most Important Skill - Dipika Tiwari

Dipika is a mid-level engineer at Qualcomm.

- **Most important skill**: efficient log analysis. Being able to quickly make sense of the bug report and provide actionable next steps is critical for productivity and trust. 
- **Advice**: keep notes as you ramp up, since that information will likely be useful later on.

Here's the full playlist asking engineers what the most important skill is for their level: [Asking 7 Levels Of Engineers The Most Important Skill](https://www.jointaro.com/playlist/J9Io5lDGaYUpI8vGrUyI/)

Helpful links:
- [How Neetcode Got Promoted From L3 to L4 (Mid-Level) Engineer At Google - Tips + Insights](https://www.jointaro.com/lesson/mP1jyI09NsCHDsi5yTEX/how-neetcode-got-promoted-from-l3-to-l4-mid-level-engineer-at-google-tips-insights/)
- Taro's top 10 to get promoted from [entry-level to mid-level engineer](https://www.jointaro.com/playlist/iBKHYpkjd0BRWWLKQG2j/)
- Taro's top 10 to get promoted from [mid-level to senior engineer](https://www.jointaro.com/playlist/AUMiBTvYUv0gcOzuu26V/)
- [Dipika on LinkedIn](https://www.linkedin.com/in/dipika-tiwari/)

[Become A Go-To Person 18/18] More Resources

- A course by [Jordan Cutler](https://www.linkedin.com/in/jordancutler1/), Senior Software Engineer at Pinterest and Author of the [High Growth Engineer Newsletter](https://read.highgrowthengineer.com/)
- Discover more from Jordan across [videos, articles, and livestreams here](https://read.highgrowthengineer.com/p/resources-and-deals?open=false#%C2%A7find-me-in-other-media).
- Get a discount on Jordan's newsletter here: <https://read.highgrowthengineer.com/taro>

Part 3 [RBP] Clean Up Your Effects

This lesson emphasizes the crucial practice of cleaning up side effects in React components to prevent unexpected behavior and optimize performance.

- **Understanding useEffect Cleanup:** Learn how to use the cleanup function returned by `useEffect` to properly handle resources like network connections, event listeners, and subscriptions.
- **Avoiding Memory Leaks and Unexpected Behavior:** Discover how neglecting cleanup can lead to memory leaks, multiple event triggers, and other unintended consequences, especially when components mount and unmount frequently.
- **Debugging with Strict Mode:** Explore how React's Strict Mode helps identify potential issues by intentionally double-invoking effects in development, making it easier to spot missing cleanup logic.

Links:

- Florian’s Next.js with Express and TypeScript Course: <https://codinginflow.com/nextjs>
- Florian’s free web development playlist on YouTube: <https://www.youtube.com/playlist?list=PLrnPJCHvNZuDG6BWjUFVxeWyYlve_SVJO>
- Code link: <https://github.com/codinginflow/react-best-practices/blob/effect-cleanup/src/app/page.tsx>

[Timeless Career Advice] How Software Engineers Create Value

Break Into Tech And Become A Software Engineer [Part 14] - Networking Without Being Weird

In this lesson, Brian emphasizes the importance of authentic online networking and provides strategies to build genuine connections without being cringy.

This lesson covers:

- Avoiding the common mistake of sending unsolicited requests for jobs or favors and focusing on building relationships instead
- Engaging with others by commenting on their posts and asking genuine questions to foster meaningful interactions
- Curating your feed by following like-minded individuals who share valuable content, ensuring that your online experience is constructive and positive

If you need extra help breaking into tech, join Brian's exclusive, cohort-based bootcamp (just 10 students per batch). Use this special link for $800 off: <https://www.parsity.io/taro>

Connect with Brian on LinkedIn: <https://www.linkedin.com/in/brianjenney/>

[Timeless Career Advice] Compounding Engineers vs Detractors

Ace The ML System Design Interview [Part 5] - Modeling

Modeling plays a critical role in machine learning system design, with classification and regression being the most frequently encountered problem types in interviews. Here are the core points from this lesson:

- Understand various evaluation metrics, such as precision, recall, F1 score, NDCG, and loss metrics like cross-entropy and focal loss, and know when to apply each
- Be prepared to discuss trade-offs between metrics, particularly precision and recall, and avoid over-optimizing for a single metric
- Recognize the significance of loss metrics, memorizing the equations for cross-entropy and focal loss, and understanding their applications in balanced and imbalanced datasets
- Be ready to discuss model selection, including architecture choices like two-tower models or matrix factorization, and anticipate follow-up questions on optimization techniques and troubleshooting issues like overfitting and vanishing gradients
- Consider a baseline model for comparison if no current system exists, and if there is an existing system, use it as a reference point

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Come Up With 500k+ User Project Ideas [Part 10] - Keep The Stakes Low

Every piece of software inevitably breaks. This is incredibly important to remember when it comes to side projects. This lesson teaches you:

- Why side projects are especially vulnerable to production issues
- Why you want the stakes for your side project to be low
- What it means to have high stakes vs. low stakes with a product, explained with a concrete example

DSA Crash Course [Part 80] - Non Adjacent Sum Walkthrough

In this lesson, Alvin walks through the python implementation for the *non_adjacent_sum* problem.

Code along and test your work in the following editor workspace: <https://structy.net/problems/non-adjacent-sum?language=py>

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

Become An Open Source Master [Part 22] - Good Practices When Making Commits

Pay attention to the naming conventions of commits and pull requests in open source projects. We also need to make sure our commits are atomic, solving one problem at a time. Always provide a clear test plan for our changes.

References:

- [One Diff, One Thesis](https://www.jointaro.com/course/level-up-your-code-quality-as-a-software-engineer/one-diff-one-thesis/)
- [Level Up Your Code Quality As A Software Engineer](https://www.jointaro.com/course/level-up-your-code-quality-as-a-software-engineer/)

Connect with James:

- GitHub: <https://github.com/jamesgpearce>
- Twitter (X): <https://twitter.com/jamespearce>

Asking Great Questions [Part 26] - Go Deeper: Follow Through

Asking great questions is a skill that takes months, if not years, to develop. So let's take the knowledge from this course and apply it! You get better by doing, not just consuming.

Take the related courses:

- [\[Course\] Level Up Your Code Quality As A Software Engineer](https://www.jointaro.com/course/level-up-your-code-quality-as-a-software-engineer/)
- [\[Course\] Effective Communication For Engineers](https://www.jointaro.com/course/effective-communication-for-engineers/)

Measure

In the **Measure** phase of the DMAIC process, we focused on building a consistent, reliable foundation for capturing and interpreting data. This phase was critical in enabling accurate decision-making and accountability across our rapidly growing organization.

### Key Takeaways:

- **We standardized measurements across the company**: To avoid confusion and misalignment, we worked for years to unify definitions, formulas, and data structures across multiple data warehouses and teams.
- **We eliminated self-serving metrics**: Jeff Bezos directed our CFO, Warren Jensen, to form an independent analytics team that would report the unbiased truth, rather than letting individual teams present metrics that suited their own narratives.
- **We invested in strong data infrastructure**: Reliable measurement required robust systems for data capture, storage, access, and reporting—along with clear accountability for maintaining these systems.
- **We built a culture of impartial data analysis**: By establishing dedicated analysts and leaders to oversee data integrity, we ensured that our business decisions were driven by objective, consistent information—not wishful thinking.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Break Into Tech And Become A Software Engineer [Part 11] - The Value Of Writing Online

In this lesson, Brian highlights the importance of documenting your coding journey and writing online, not for fame but for personal growth and community building.

This lesson covers:

- Writing online as a way to document your progress, overcome imposter syndrome, and differentiate yourself in a crowded job market
- How being genuine in your writing attracts like-minded people and builds a valuable network
- The long-term benefits of writing consistently, even without immediate engagement, and how it helps organize your thoughts and improve communication skills

If you need extra help breaking into tech, join Brian's exclusive, cohort-based bootcamp (just 10 students per batch). Use this special link for $800 off: <https://www.parsity.io/taro>

Connect with Brian on LinkedIn: <https://www.linkedin.com/in/brianjenney/>

Tech Lead Blueprint 8/17 - Delegating The Work

Connect with Lalit on LinkedIn: <https://www.linkedin.com/in/lalitkundu/>

[Part 7] Meta Intern Success Series - Succeeding As A Remote Intern

- Learning speed is severely nerfed by remote work, which is awful for you as an intern since this is when learning speed is the most important. It's important to keep this mind as you navigate your internship.
- Attend all team events, even the less formal ones (i.e. non-offsite) like team lunch. It's extremely important to build relationships and social capital in this environment where everyone is more isolated.
- Have 2 to 3 1:1s with your non-intern manager peers.
- Match the working hours of your team if you're in different time zones. It's absolutely vital that you always get blocked ASAP, and this is way harder when you're operating on a different time scale. This could be painful, but it will be worth it.
- Do virtual coworking whenever you can. The idea is that everyone joins a video call and just works, no meeting agenda or anything. The idea is to mimic an in-person working environment as much as possible. This creates peer pressure for you to be productive and makes it seamless for you to ask for help (just say something on the call!).

Asking Great Questions [Part 9] - Be Appreciative

The people who give the best help often have some degree of altruism. When you learn how to appeal to that altruism, you supercharge your chances of getting great answers quickly on your questions. This lesson teaches you:

- How many engineers view showing appreciation incorrectly, seeing it as a transactional action instead of a proactive action
- When to show appreciation proactively
- What it looks like weaving appreciation into your question, explained with an in-depth example
- A lazy way of showing "appreciation" that you should definitely avoid by spending an extra 10 seconds

What Big Tech Data Structures And Algorithms Interviews Generally Look Like

FAANG interviews are some of the most well-defined in the industry. However, this style of software engineer interview is often completely failed due to a lack of preparation. Here's what to expect when it comes to data structures and algorithms interviews for Big Tech.

Here are the core points from the video:
- **Phone screens are Leetcode easy and medium** - This will either be 2 easy problems or 1 medium problem. This means that if you haven't progressed to onsite rounds yet, don't spend much time on Leetcode hard problems: That's a premature optimization. Make sure you have your easy and medium problem mastery down, so you can even make it to that final round.
- **Onsites are Leetcode medium and hard** - This will either by 2 medium problems or 1 hard problem. If you receive a problem that's more on the medium side, make sure to budget your time well. There's a good chance the interviewer wants you to solve another problem on top of that.

Related resources:
- [[Masterclass] How To Ace Your Big Tech Interview - Data Structures & Algorithms](https://www.jointaro.com/lesson/V4sTACzfOh22nMVcUV3w/how-to-ace-your-big-tech-interview-data-structures-and-algorithms-7172021/) - This is the full video this clip is taken from
- [[Masterclass] How To Ace Your Big Tech Interview - System Design](https://www.jointaro.com/lesson/J3I9fTW9ulr6T9KjxpqY/how-to-ace-your-big-tech-interview-system-design-1162021/)

Tech Career Growth Videos

Our instructional videos provide software engineers with insights needed for career growth. Masterclasses go incredibly in-depth on a specific tech topic while case studies are real examples from established engineering leaders.