How Managers Hire Great Engineers [Part 20] - Tips For Interviewing

It's critical as a hiring manager to make sure that an engineer candidate is the right fit during the interview. From this lesson, you will:

- Learn to avoid hypothetical questions in favor of inquiries about concrete experiences to gain accurate insights into a candidate's actual work behavior
- Understand the importance of building rapport and appearing friendly throughout the interview process, as candidates are also evaluating their fit with the team
- Discover the value of maintaining a curious mindset, encouraging thorough exploration of a candidate's responses, especially when initial impressions may suggest a red flag

Connect with John Egan on LinkedIn: <https://www.linkedin.com/in/jwegan/>

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!

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).

Maximize Your Equity And Benefits In Tech [Part 19] - Pre-Tax vs. Roth

Choosing between pre-tax and Roth 401(k) contributions can significantly impact your long-term tax outcomes. Here are the core points from the lesson:

- The net tax advantage between pre-tax and Roth depends on your current vs. future tax rates; Roth may be ideal if you're in a lower bracket now, but pre-tax offers flexibility for future low-tax Roth conversions
- Roth IRAs offer penalty-free access to contributions anytime, while Roth 401(k)s have stricter withdrawal rules, often treating withdrawals as a mix of contributions and earnings
- Pre-tax retirement accounts are subject to Required Minimum Distributions (RMDs) starting at age 73, which can force taxable income in retirement
- Doing Roth conversions during low-income years (e.g., early retirement) can reduce future RMD burdens and smooth your tax liability over time

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/)

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)

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>

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)

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.

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!

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.

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.

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

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/>

Maximize Your Equity And Benefits In Tech [Part 10] - Strategy (ESPP)

ESPPs can offer significant value, but deciding when to sell requires more nuance than it may seem at first glance. Here are the core points from the lesson:

- If there’s no holding period requirement, selling immediately after purchase can yield guaranteed gains, especially with discounts and lookback provisions
- Holding ESPP shares introduces market risk and complexity, and your decision should reflect whether you’d buy the stock outright if given the equivalent in cash
- A holding period aligned with qualified disposition rules may offer better tax treatment—but not always; in some cases, a disqualifying disposition results in lower overall tax
- When stock declines during the offering but rebounds before sale, the tax owed under a qualified disposition may be higher due to how ordinary income is calculated from the offering price rather than purchase price

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/)

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.

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/>

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

Have A Conversation About Conversation

In this lesson, we explore how to set up a healthy, effective relationship with an engineering manager by having a “meta” conversation early—one that focuses not on tasks, but on how we want to work together.

- We introduce the idea of a personal “README file,” where we proactively explain our background, working style, motivations, and concerns so our EM understands how to support us.
- We emphasize sharing what energizes us and what drains us, giving our manager the context they need to advocate for us and assign work that helps us thrive.
- We suggest regularly reflecting on what’s going well and what isn’t—using 1:1s not just for status updates, but for deeper calibration on collaboration.
- We call out the importance of explicitly discussing how we prefer to receive (and give) feedback, whether direct and verbal or written and reflective.
- We highlight that starting the relationship with these meta conversations sets the foundation for a smoother, more aligned partnership with our EM.

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/>

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.

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.