Maximize Your Equity And Benefits In Tech [Part 7] - Other Considerations (RSUs/Bonuses)

Even with a solid RSU strategy in place, there are a few overlooked tax and compliance details that can complicate things. Here are the core points from the lesson:

- Insider trading policies may limit your ability to auto-sell or manually sell RSUs immediately upon vesting, leading to short-term gains or losses you can’t always control
- Wash sale rules can disallow losses if you repurchase the same stock within 30 days of a sale, which can easily happen with monthly vesting schedules
- Incorrectly reporting RSU-related wash sales on your taxes may go unnoticed, but technically violates IRS rules and can affect your cost basis going forward

Next steps with Andy:

- [Connect with Andy on LinkedIn](https://www.linkedin.com/in/andymoranio)
- [Leave feedback and/or sign up for Andy’s newsletter](https://subscribepage.io/taroequitycourse)
- [Schedule a consultation](http://adastrafp.com/contact-us/)

Taro Book Club: Designing Data Intensive Applications - Chapter 8 (Distributed Systems)

Topic: Chapter 8 - The Trouble with Distributed Systems

In this #taro-bookclub discussion, we'll go over Chapter 8 of [**"Designing Data-Intensive Applications"**](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321) by Martin Kleppmann.

Here's the agenda:

- We’ll break down and discuss the key concepts from Chapter 8.
- There will also be time for any questions and answers you might have.

Please send your questions ahead of time to [**Rohit Katyal**](https://www.linkedin.com/in/rohitkatyal/) in the [**Taro Premium**](https://www.jointaro.com/membership/) Slack, so that they can be included in the discussion. *If you don't have the book or want more details on how this all works, check out the #taro-bookclub channel in the Taro Premium Slack as well!*

Get ready for another engaging and informative session!

Expert Secrets of Successful Big Tech Interviews

**Event description**

Join this special NEW session where I interview "Mr. X" –– a former Amazon Bar Raiser and current Head of Talent Acquisition at a top tech company that must remain nameless ;)

Together, we'll take you beyond the basic tips you already know and unveil **specific, winning strategies** that actually separate you from other candidates in competitive interviews.

If you have the job skills and a solid work history (but interviewing continues to be where you "fall down" in the job search) this event will be an absolute game-changer.

**After This Masterclass, You Will:**

- **Eliminate Blind Spots**

  - **You’re making mistakes every time you interview, that you don’t see, that cost you job offers. Get ready to hear what they are.**

- **Own Your Story**

  - **The winning candidates aren’t necessarily the most skilled––they have the best stories. Soon, YOU will be that candidate.**

- **Read Minds (Literally)**

  - **Hiring managers speak in code. What they say is often totally different from what they’re thinking. Now you’ll know the difference.**

If you need additional support with your job search, you can get coaching from Alan and Kadima Careers. Join with a discount here: [**https://signup.kadimacareers.com/mQBsDvu**](https://signup.kadimacareers.com/mQBsDvu)

How Managers Hire Great Engineers [Part 7] - LinkedIn Search

If you work in tech, it's impossible to escape LinkedIn and this applies for engineering hiring as well. From this lesson, you will:

- Learn why LinkedIn search is a go-to tool for sourcing candidates and why a LinkedIn premium subscription is recommended for engineering managers
- Understand the limitations of LinkedIn’s free tier and the benefits of upgrading to LinkedIn premium, including unlimited search results and in-mail credits
- Explore the differences between LinkedIn Premium and LinkedIn Recruiter and why LinkedIn Recruiter may not be cost-effective for engineering managers
- Gain practical tips on how to use LinkedIn search effectively, including filtering by title, company, and school, and leveraging Boolean searches to refine results
- Discover how to maximize the value of your existing network by searching through first and second-degree connections before expanding your search

Connect with John Egan on LinkedIn: [https://www.linkedin.com/in/jwegan/​](https://www.linkedin.com/in/jwegan/%E2%80%8B%E2%AC%A4)

Peer Pressure

Working in Big Tech often means operating in a highly competitive environment filled with extremely talented peers. This creates both motivation and significant pressure, especially due to how performance systems are designed.

- Big Tech companies commonly use forms of stack ranking, where engineers are compared against peers and the bottom 5–15% may face probation (PIP) or termination each review cycle.
- Being surrounded by exceptionally strong coworkers can lead to intense pressure, imposter syndrome, and constant anxiety about staying out of the bottom cohort.
- High performers are often rewarded well, but lower performers may struggle, making the environment feel “dog-eat-dog.”
- Talent density can create problems with limited scope and many strong engineers competing for meaningful work, which can impact evaluations and career growth.
- Some teams have abundant scope and are great for growth, while others (like highly mature products) may have minimal impactful work, worsening peer pressure.

Taro Book Club: Designing Data Intensive Applications - Chapter 4 (Encoding And Evolution)

Session Led by: [Lax Meiyappan](https://www.linkedin.com/in/lakshmanan-meiyappan/)

Topic: Chapter 4 - Encoding And Evolution

In this #taro-bookclub discussion, we'll be talking about Chapter 4 of [**"Designing Data-Intensive Applications"**](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321) by Martin Kleppmann.

Here's the agenda:

- Let’s get to know each other with some quick introductions.
- We’ll break down and discuss the key concepts from Chapter 4.
- There will also be time for any questions and answers you might have.

Please send your questions ahead of time to [**Lax Meiyappan**](https://www.linkedin.com/in/lakshmanan-meiyappan/) in the [**Taro Premium**](https://www.jointaro.com/membership/) Slack, so that they can be included in the discussion.

Get ready for another engaging and informative session!

Poor Work-Life Balance

Working in Big Tech offers prestige and high pay, but it often comes at the cost of personal time and mental well-being. The demanding environment, peer pressure, and ambiguous policies can lead to overwork and burnout.

- **Competitive Culture Fuels Overwork**: The intense stack ranking and peer comparison systems push employees to work beyond normal hours to maintain high performance ratings and avoid being ranked at the bottom.
- **On-Call Stress and Burnout**: At scale, systems often encounter issues, and engineers may feel pressured to respond even during vacations, especially if they are domain experts.
- **Peer Pressure to Match Hours**: Engineers often feel compelled to match their teammates’ late-night work habits, contributing to a cycle of poor boundaries and collective burnout.
- **Unlimited PTO Backfires**: Policies like “unlimited vacation” often discourage time off, especially when company leadership sends contradictory messages about taking breaks.
- **Regional Differences Exist**: The culture of overwork is more intense in Silicon Valley compared to other parts of the world, such as Europe, where vacations are taken more seriously and work-life boundaries are better respected.

Before you challenge them, ask if they trust you yet.

You’ve seen this happen.\
A good idea, pushed too soon—and everything shuts down.

This section gives you the signal to look for before you push.\
And what to do if it’s not there yet.

**Want to go deeper?**

- Explore more tools and courses at [**Taha’s Method**](https://www.tahasmethod.com/) — from interview prep to leadership development.

- Follow me on [**LinkedIn**](https://www.linkedin.com/in/tahahussain/) and subscribe to my weekly newsletter, [**The Conscious Leader**](https://tahahussain.substack.com/), for more lessons like these.

- Get 30% off [**Top Tech Leader**](https://bit.ly/tarotopleader) with code **ILOVETARO** (exclusive for Taro members).

Future Job Prospects

Having a Big Tech name on your resume, whether Meta, Google, Uber, or others, often leads to significantly improved job-seeking outcomes. This reputation, deserved or not, dramatically influences how recruiters and hiring managers perceive candidates.

- **Stronger Resume Signal**: A stint at a Big Tech company increases perceived credibility and opens doors. Even a one- to two-year tenure can 100x the likelihood of landing interviews at other desirable companies.
- **Perception of Operational Excellence**: Recruiters and startup founders often assume that employees from companies like Amazon or Meta carry with them the systems, processes, and best practices that helped those firms succeed.
- **Proximity to Core Value Drives Perception**: Engineers who worked on flagship products or core systems (e.g., TikTok’s recommendation engine) are perceived as especially valuable, as if they embody the company’s strategic advantage.
- **Brand Halo Effect**: Just being associated with a known entity creates a trust shortcut—recruiters assume a baseline of competence and quality due to the rigorous hiring practices and peer environment of Big Tech.
- **Standardized Hiring Signal**: The structured interview process (e.g., Leetcode-style DSA questions) at these companies helps ensure a consistent bar, reinforcing the belief that Big Tech engineers are generally stronger than a randomly selected candidate.

Ship & Grow 500k+ User Side Projects [Part 18] - GitHub

95%+ of software engineers aren't getting nearly enough value from GitHub, which is a shame as they're already using it. This lesson teaches you:

- Why it's so easy to stand out on GitHub
- How GitHub is becoming more and more powerful as a traffic-driving mechanism
- How to quickly make an awesome README to greatly improve your repo's attractiveness
- What a terrible GitHub README looks like, explained with a fun example
- What great GitHub READMEs look like, explained with 2 examples
- The ingredients you should have for a compelling GitHub README

Here are the repos shared in the lesson:

- <https://github.com/tsenart/vegeta>
- <https://github.com/afollestad/material-dialogs>

Stability

Even amid recent waves of layoffs, Big Tech companies continue to offer more job stability than most others in the tech industry. This stability is driven by several structural advantages, not just current economic conditions.

- **Layoffs in Big Tech are more reactive, less  routine**: Unlike smaller companies or traditional enterprises that cut staff regularly every 1–2 years, Big Tech companies like Google and Facebook went over a decade without mass layoffs. Their recent cuts were responses to extreme macroeconomic shifts, not standard operating procedure.
- **Higher baseline of job security**: Statistically, it’s harder to lose a job at a Big Tech company, especially when compared to \~80–90% of other tech firms. That said, consistent performance is expected to maintain that security.
- **Stability comes from product-market fit**: These companies have already achieved PMF with multiple products—Google, Meta, Airbnb, etc.—which ensures reliable revenue and customer demand. This reduces existential risk compared to early-stage startups.
- **Strong financials provide a cushion**: Many Big Tech firms operate some of the most profitable business models in history, particularly in digital ads and cloud infrastructure. Their massive cash reserves and consistent profit margins further insulate them from financial instability.
- **Comparison with startups is stark**: Companies still seeking product-market fit or surviving on runway from funding rounds face constant pressure to cut costs, making layoffs more common and less predictable.

Amazon Behavioral Interview [Part 27] - Recovering From A Bad Decision (Answer)

This video segment describes a situation where the interviewee made a mistake in their role at American Express.

- **Identified the Mistake:** The interviewee implemented a new incentive system for customer care professionals that rewarded them for saving high-value customers while neglecting lower-value customers. This led to:

  - **Cherry-picking of customers:** Customer care professionals prioritized high-value customers, leading to long wait times for lower-value customers.
  - **Negative customer experience:** Long wait times resulted in poor customer satisfaction and potentially damaged brand reputation.
  - **Missed revenue opportunities:** Neglecting lower-value customers, particularly early-tenure customers, resulted in missed opportunities for future revenue growth.

- **Learned from the Mistake:** The interviewee learned the importance of:

  - **Considering all stakeholders:** Involving customer care professionals in the decision-making process.
  - **Thinking long-term:** Considering the long-term value of customers, not just their immediate spending.
  - **Gathering diverse perspectives:** Seeking input from former team members to identify potential pitfalls.

- **Implemented Corrective Actions:**

  - Revised the incentive system to account for customer lifetime value.
  - Implemented measures to improve call center response times and reduce wait times.
  - Developed a system to identify and address cases of long wait times for customers.

This example demonstrates the interviewee's: 

- ***Self-awareness:*** *Acknowledging and taking responsibility for their mistake.* 
- **Problem-solving skills:** Identifying the root cause of the issue and implementing corrective actions. 
- ***Learning agility:*** *Learning from the mistake and applying those learnings to future projects.* 
- **Focus on customer experience:** Prioritizing customer satisfaction and long-term customer relationships. 
- **Continuous improvement:** Continuously refining processes and seeking ways to improve performance.

Learn more about Alan's job search program, Kadima Careers, using this [discount link](https://signup.kadimacareers.com/mQBsDvu).

Become An Engineer Influencer [Part 5] - Top-Funnel Channels

Startup Employee To Startup Founder: What I Wish I Knew

From 2013 to 2023, Tongbo Huang worked as an engineer or manager at three high-growth companies: Pinterest, Chekr, and Amplitude.

After 10 years working in startups of various sizes, Tongo decided to start his own: [**Sidr Tax**](https://www.sidr.io/). In the past year, Sidr Tax has surpassed 6 figures of revenue and is growing rapidly.

In this talk, Tongbo will share:

- How he thought about taking funding (he previously went through Y Combinator) and why he decided to bootstrap for Sidr.
- How he ended up working on a tax startup and how Shopify is fueling his growth.
- The problem with founders today looking for "exciting" ideas.

Connect with Tongbo here: [**https://www.linkedin.com/in/tongbohuang/**](https://www.linkedin.com/in/tongbohuang/)

\[Taro Top 10\] Entrepreneurship And Tech Startups: <https://www.jointaro.com/playlist/ArtlSzF17v7cad1K6TJG/>

For SWE To ML Transitioners

In this final section on preparation strategy, we focus on how to transition successfully from software engineering into machine learning. Drawing on real experiences, we highlight practical, effective ways to stand out.

- **Lead with your strengths**: We already bring strong system design, coding, and production skills—qualities ML teams highly value. Hiring managers often prefer engineers moving into ML over scientists moving into engineering.
- **Leverage your current role**: Instead of relying only on side projects, we can grow by contributing to ML efforts at work—like model deployment, pipelines, or feature engineering.
- **Be selective with side projects**: One well-scoped project with clear business impact is far more compelling than several small tutorials.
- **Demonstrate your learning journey**: Show, don’t just tell—mention coursework, hands-on coding, and real applications of ML in your work.
- **Be honest and self-aware**: Interviewers appreciate curiosity and a growth mindset more than perfection. Let’s share where we are, what we’re learning, and how we’re applying it.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

In this segment, we explore the importance of **Exploratory Data Analysis (EDA)** in machine learning interviews, especially during notebook-based exercises. This is a key moment to distinguish ourselves by demonstrating analytical depth and real-world intuition.

- We move beyond just **checking for missing values or printing basic stats**, which signals a surface-level approach.
- Instead, we analyze **feature distributions**, identifying skewness, outliers, and unusual patterns that may affect modeling.
- We examine **relationships and interactions between variables**, not just computing correlations, but exploring potential dependencies and dynamics.
- We assess **data quality**, including duplicates, inconsistent categorical values, and signs of data leakage.
- This deeper EDA shows that we can **interrogate the data critically**, a trait that interviewers associate with strong, production-ready ML practitioners.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Explanation Examples

In this extended segment, we see the CLEAR framework applied to real machine learning interview questions. Through examples, we’re shown how to craft strong, structured answers that go far beyond buzzwords or surface knowledge. This approach helps us communicate our understanding with clarity and depth.

- For a question like **"How do decision trees work?"**, we learn to walk through each part of CLEAR: define the concept, link to related ideas (e.g., random forests), provide real-world use cases, weigh pros and cons (e.g., interpretability vs. overfitting), and explain evaluation metrics (e.g., accuracy, RMSE).
- When comparing **CNNs vs. RNNs**, we avoid vague statements and instead explain each model’s core function, typical applications (e.g., image classification vs. time-series forecasting), and evaluation metrics, even including hybrid use cases for extra depth.
- For the common question **"How do you handle class imbalance?"**, we’re shown how to define the issue, link it to broader modeling challenges, provide realistic examples (e.g., fraud detection), explore trade-offs in techniques like resampling and reweighting, and select meaningful metrics like precision, recall, and AUC-ROC.
- We’re reminded that **bad answers often list jargon** without context or reasoning, while **great answers show structured thought**, connect theory to practice, and demonstrate the ability to make sound trade-offs.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Become An Engineer Influencer [Part 10] - Story-Telling

You'll Learn Proper Engineering Skills

Working at Big Tech companies offers a unique opportunity to develop strong engineering foundations by being immersed in mature processes, robust tools, and a culture of high standards.

- **Access to top-tier engineers and tooling**: Employees are surrounded by highly skilled engineers and comprehensive internal tools that streamline development. Many of these tools address needs engineers didn’t even know they had until using them.
- **High-quality code review culture**: Rigorous code reviews from experienced peers serve as a fast-track for learning. Early feedback can be intense but leads to rapid growth in code quality, scalability awareness, and best practices.
- **Exposure to mature engineering systems**: Engineers learn the full lifecycle of software deployment with components like dogfooding, alpha/beta testing, stage rollouts, A/B testing, monitoring, and alerting, many of which are often missing or underdeveloped in smaller companies.
- **Longevity drives technical evolution**: Companies like Meta and Google have evolved with the industry, migrating through major tech stack changes over decades. This adaptability teaches engineers how to future-proof their systems and processes.
- **Cultural emphasis on continuous learning**: The engineering culture encourages innovation, resilience, and cross-pollination of ideas, making it ideal for those looking to grow as adaptable and forward-thinking software engineers.

SWE Internship Survival Guide [Part 18] - Plot Your Growth Trajectory

Wrapping up all the advice and strategies into something actionable can feel overwhelming—so this video offers a clear, week-by-week roadmap to help you stay on track throughout your internship. Here are the core points from the lesson:

- **Weeks 1–2**: Embrace your newbie status—ask lots of questions, explore the codebase, and focus on learning, even if it means breaking things (safely)
- **Weeks 3–4**: You should start contributing code more regularly, with fewer basic mistakes in PRs, and begin transitioning from onboarding tasks to your main project
- **Weeks 5–7**: You’re gaining confidence—PR feedback is more meaningful, your code is smoother, and a midpoint check-in should help confirm you’re on track
- **Weeks 7–10**: You’re hitting your stride—code velocity and quality are high, you’re asking sharper questions, and maybe even helping others on the team
- **Weeks 11–12**: Finalize your project, document it, reconnect with your network, and take a moment to enjoy the end of a hard-earned, productive summer

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.