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.

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!

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).

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/>

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

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/>

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/>

How Managers Hire Great Engineers [Part 17] - Push & Pull Factors

It's hard convincing great engineers to leave their current jobs. From this lesson, you will:

- Understand the importance of increasing your candidate acceptance rate, which can significantly reduce workload throughout the recruitment funnel
- Learn how to identify and leverage push and pull factors that influence a candidate's decision to leave their current role or join your company
- Discover the value of tailoring your closing approach to individual candidate motivations to enhance offer acceptance rates
- Gain insights into conducting honest Q&A sessions during the closing call to ensure a good fit, thereby increasing long-term retention on your team

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

Become An Engineer Influencer [Part 7] - Mid-Funnel Channels

Round Structure ML System Design

In this segment, we’re introduced to one of the most critical and career-defining parts of the ML interview process: **system design**. Especially at senior and staff levels, this round reveals our ability to lead and think holistically.

- **System design interviews evaluate both breadth and depth**—we’re expected to understand the full ML pipeline from data ingestion to deployment and monitoring.
- This round is less about solving isolated problems and more about **architecting end-to-end solutions** that operate under real-world constraints.
- We must demonstrate our ability to **make trade-offs** involving scalability, latency, noisy data, evolving product requirements, and long-term maintenance.
- Strong performance here often **distinguishes mid-level candidates from senior or leadership-level hires**.
- Ultimately, it’s our chance to show that we can **design ML systems that work, scale, and deliver value in production—not just in theory**.

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/>

Maximize Your Equity And Benefits In Tech [Part 17] - 401(k) And IRA Basics

Understanding retirement accounts is essential for long-term financial planning. Here’s the core points from the lesson:

- 401(k)s allow automated payroll contributions (up to $23,500 in 2025 for those under 50), often include employer matching, and offer pre-tax or Roth contribution options
- Contributions reduce taxable income but are still subject to payroll taxes like Social Security and Medicare
- IRAs (individual retirement arrangements) are similar to 401(k)s but are personally managed, have lower contribution limits, and don’t include employer matching
- IRA contributions may be tax-deductible or Roth-eligible depending on your income, unlike 401(k)s which have no income-based restrictions for pre-tax contributions

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/)

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.

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/>

Just A Cog In The Machine

Although Big Tech offers compensation and prestige, the sheer scale of these organizations can lead engineers to feel like small cogs in an enormous machine. The work often becomes highly specialized and incremental, making it harder to find personal fulfillment or a sense of ownership.

- **Incremental Work Dominates**: Many engineers are tasked with refining already hyper-optimized features, such as tweaking a single call-to-action button on an ad format. This kind of work can feel repetitive and lacking in creative fulfillment.
- **Limited Ownership**: As teams grow larger, individual engineers may end up owning only a sliver of a product component—sometimes just one platform’s implementation of a single UI element.
- **Slower Innovation in Mature Products**: Legacy systems and products that have been around for over a decade (like Gmail) may offer fewer opportunities for impactful changes, making it harder to grow scope or gain recognition.
- **High Competition for Scope**: Even when surrounded by brilliant engineers, opportunities for meaningful ownership can be scarce. This leads to competition over small improvements and sometimes a struggle to stand out.
- **Pockets of Growth Still Exist**: Certain high-growth teams such as those working on newer initiatives like AI or experimental products can still offer dynamic, zero-to-one opportunities. Success often depends on finding and joining these pockets within the broader organization.

Amazon Behavioral Interview [Part 11] - Tell Me About A Time You Exceeded Expectations (Answer)

This video segment describes the interviewee's experience successfully increasing the acquisition of a business credit card at Costco.

- **Identifying an Opportunity:** The interviewee recognized the untapped potential of the business card, which offered higher profitability compared to the consumer card. They also identified a key differentiator: a higher gas rebate for the business card.
- **Leveraging Internal and External Factors:** The interviewee capitalized on the increased warehouse incentive for selling business cards and secured additional funding for training and in-store marketing.
- **Overcoming Challenges:**
  - **Internal Competition:** The interviewee faced resistance from the consumer card division, who perceived the increased business card acquisition as cannibalizing their own customer base.
  - **External Constraints:** The interviewee navigated challenges related to Costco's limited marketing space and restrictions on in-store promotions.
- **Significant Results:** Despite these challenges, the interviewee successfully tripled the initial acquisition target, demonstrating strong project management and execution skills.

This example highlights the interviewee's ability to identify and capitalize on opportunities, overcome internal and external challenges, and achieve significant results.

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

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

Become An Engineer Influencer [Part 14] - Tips on Writing

When Should I Actually Use AI During The Interview?

In this lesson we talk about when and how to use AI effectively during an AI-assisted coding interview.

- **Find the right balance with AI:** Learn how to avoid over-delegating core logic to AI or underusing it and running out of time, by aiming for the “middle path” that maximizes leverage while minimizing risk.
- **Accelerate typing, not thinking:** Discover why you should use AI for scaffolding, boilerplate, and clearly defined test cases—but retain ownership over algorithms, data structures, and key design decisions.
- **Optimize for easy verification:** Understand that AI is most helpful when you already know what needs to be built and can quickly verify the output, and least helpful when you’re still figuring out the correct approach.

Ship & Grow 500k+ User Side Projects [Part 20] - When To Give Up

Side projects can give you massive, career-changing, *life-changing* benefits. However, in order for this to happen, you need to put in a lot of work. This lesson teaches you:

- The balance you need to strike when it comes to working on side projects, particularly which ones you work on
- The minimum amount of time you need to give a side project that you genuinely believe in
- A detailed list of criteria you can use to figure out whether you should abandon a side project
- Why failed side projects are actually good for you

Taking Things Further

In Part 4 of the course, we focus on how to **embed "thinking in deltas" as a daily leadership habit**. This final module emphasizes how to continuously spot and create positive, sustainable change as a manager—not just react to problems.

- **We build the habit by journaling our daily deltas**, capturing the tangible changes we influence, however small.
- **We use the journal to reframe how we think**, making it easier over time to spot opportunities for impact in individuals, teams, and systems.
- **We improve our mental well-being as managers** by recognizing our contributions and wins in a role where feedback loops are long and ambiguous.
- **We measure our success not just by outputs, but by meaningful changes we've catalyzed**—deltas we can point to as outcomes of our leadership.

Creating a daily "delta journal" isn’t just about reflection—it’s how we grow our impact and resilience as leaders.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

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.