Why Layoffs Happen

We explain why layoffs happen and why they’re a recurring reality in the tech industry.

Here are the core points from the lesson:

- Layoffs occur when companies face bad economics, often triggered by unforeseen macroeconomic shifts like the post-COVID slowdown after years of stimulus-fueled growth.
- Overambitious hiring is a major contributor, especially when overly positive economic conditions lead companies to scale headcount faster than the business can sustainably support.
- Fundamentally unsound business models and poor strategic direction also drive layoffs, and in practice, these factors usually overlap—making layoffs inevitable since executives can’t make perfect decisions all the time.

Team Importance

We explain how to evaluate whether a specific *team* within a company is a strong place for growth, impact, and stability.

Here are the core points from the lesson:

- We show how to assess whether a team is core to the business by asking whether it’s a profit center or cost center; teams tied to revenue (like Google Ads) offer stability and long-term opportunity, while peripheral or internal-tool teams often limit growth.
- We illustrate how to categorize teams: mature core teams (stable, slower growth), big bets (high upside but real risk of shutdown, like Stadia), and low-impact internal teams (comfortable but slow-growth).
- We recommend questions that reveal real importance: What does success look like? What growth targets matter? Does the CEO mention this product in earnings calls? These signals show whether leadership actually prioritizes the work.
- We show how to “backdoor” the truth by asking leaders their top three priorities; if your team isn’t on that list, it’s unlikely to be a strong engine for your career unless you explicitly want a lower-pressure role.

Challenge Yourself, Be Authentic

This closing segment reinforces the importance of authenticity and adaptability during the interview process, especially in ML interviews where surprises are common.

- **Be ready for the unexpected**—interview questions may push you outside your comfort zone, but your response and thought process matter more than perfection.
- **Think out loud**—articulate your assumptions, logic, and uncertainties to help interviewers understand how you approach problems.
- **Show your passion**—talk about what genuinely excites you in ML, whether it's recommender systems, data scale, or experimentation.
- **Connect your interests to the company’s mission or team goals**—this helps show alignment and genuine motivation.
- **Remember, interviewers are hiring a person, not just a resume**—let your curiosity and drive come through.

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/>

Effective Preparation Tips

In this segment, we get practical advice on how to **efficiently prepare for the coding rounds** in ML interviews, especially if we’re coming from a software engineering background. The message is clear: strong coding fundamentals are essential—and expected.

- We should master **core data structures and algorithms**—like arrays, hash tables, trees, graphs, recursion, BFS/DFS, and dynamic programming—as these show up frequently in ML interviews.
- **Time and space complexity analysis** is crucial; interviewers want to see not just working solutions, but scalable ones.
- For big tech companies like Meta, Google, and Amazon, we should focus on **medium-difficulty LeetCode problems**, which strike the right balance of challenge and relevance.
- **Company-tagged questions** on LeetCode, while not perfect, can be surprisingly accurate, especially for smaller firms reusing standard problem sets.
- The key reminder: **ML roles don’t have a lower coding bar**—interviewers expect the same rigor as they do for software engineering roles. Prepare accordingly.

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/>

Common ML Behavioral Questions

Here's a focused summary of the types of questions we should expect in the **ML behavioral and project deep dive interview round**, with an emphasis on communication, problem-solving, and cross-functional thinking.

- We’ll often be asked to **describe a challenging ML project**—this is our chance to walk through the problem, solution, and business impact in a structured story.
- Expect to explain **how we improved model performance**, including what metrics we tracked, the changes we made, and the quantifiable outcome.
- Be prepared to discuss **failures and setbacks**—interviewers want to hear how we responded, what we learned, and how we iterated or recovered.
- We’ll likely need to **explain ML concepts to non-technical stakeholders**, showcasing our ability to communicate complex ideas clearly and persuasively.
- For ML infrastructure roles, we should be ready to talk about **frameworks or tools we built**, the **scaling challenges** we solved, and how we enabled productivity for other engineers or data scientists.

In every answer, we should highlight not just *what* we did, but *how* we thought, the **trade-offs** we considered, and **what we learned**.

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/>

Algorithm Coding Quality Tips

In this wrap-up of the ML algorithm coding interview section, we focus on what truly elevates our performance beyond just getting a working implementation. Code quality, structure, and composure matter just as much as correctness.

- We should **structure our code clearly**, breaking it into logical methods like `fit`, `predict`, and `update_centroids` instead of writing one long script.
- **Clean abstractions** not only make our solution easier to understand, but also easier to debug and maintain—key traits of strong engineering.
- While full optimization isn’t expected, we must **show awareness of time and space complexity**, and briefly discuss trade-offs where relevant.
- If time allows, we should include **test cases using toy data**, showing that we think like engineers who validate their code.
- If our code breaks, we demonstrate a **calm, methodical debugging mindset**, which interviewers value as highly as technical correctness.

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/>

Hiring Criteria

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

This section of the video explains how the engineering manager (EM) interview process differs significantly from that of individual contributor (IC) roles. It emphasizes that EM interviews are more focused on leadership behaviors and decision-making rather than deep technical assessments.

- **Hiring focus shifts significantly:** Unlike IC interviews where 80% of the evaluation is based on technical competence (e.g., DSA and system design), EM interviews flip this ratio — **80% of the evaluation is based on behavioral and leadership skills**.
- **Technical leadership is the focus:** The primary aim is to assess whether a candidate can **lead teams, manage stakeholders, plan roadmaps, and coach effectively**, rather than just solve technical problems.
- **Trust is central:** Behavioral interviews seek to answer one key question: **Can this person be trusted to ensure the team delivers, no matter what?**
- **Technical evaluation still exists but is broader:** The remaining 20% of the EM interview process tests a candidate’s ability to **handle high-level technical topics**, often through system design or architecture discussions rather than deep algorithmic challenges.
- **Successful EM candidates demonstrate strategic thinking and people management**, not just strong coding skills. Knowing how to translate engineering outcomes into business impact is critical.

What Order Should You Study Data Structures & Algorithms?

Instead of doing random problems, you need to structure your DSA prep to tackle concepts in the right order.

Course: <https://www.jointaro.com/course/crash-course-data-structures-and-algorithms-concepts/>

How To Do Research

We walk through how to actually evaluate companies and teams, emphasizing that good information is hard to find and rarely lives in public reviews.

Here are the core points from the lesson:

- We stress that big companies aren’t monolithic—cultures vary widely across thousands of teams—so the most reliable way to assess a company or team is to tap into our network, where trusted people can share accurate, firsthand experiences.
- We highlight why building deep relationships in school and at work is invaluable: our network expands our access to real data over time, and meaningful connections (teammates, managers, peers) often shape our opportunities more than technical ability alone.
- We suggest complementary methods—reverse-interviewing during the hiring process, reading engineering blogs, following tech news, and checking curated communities—to gather signal, while warning that Glassdoor skews too positive and Blind skews too negative due to self-selection.
- We show that even venture capital, which allocates billions in tech, runs almost entirely on networks and backchannel references—underscoring that our own career decisions benefit from the same relationship-driven information flow.

Engineering Manager Misconceptions

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

This segment dispels a common myth in engineering leadership: that an engineering manager must be the most technically knowledgeable person on the team. Instead, it reframes technical leadership as an exercise in empowerment and strategic growth—both for the team and for the manager.

- **Technical mastery is not a requirement for effective engineering management.** The core responsibility is ensuring delivery, not being the smartest person in the room.
- **Empowering the team to deliver independently** fosters a stronger, more resilient team and allows individual contributors to grow.
- **A high-functioning team that performs well without direct intervention** is a strong reflection of a manager’s leadership, not a sign of irrelevance.
- **Delegating technical ownership frees the manager to focus on bigger-picture strategic initiatives**, technical direction, and organizational influence.
- **Letting go of ego is essential**—leaders grow by creating impact at scale, not by micromanaging every decision or technical detail.

Common Follow-up Questions

In this final part of the section, we’re reminded that **follow-up questions** in live ML interview rounds are crucial opportunities to demonstrate deeper thinking. How we respond after our initial solution can significantly elevate the impression we leave.

- When asked about **model improvement**, we show we understand what enhances performance—like adding features, collecting edge-case data, or exploring advanced models.
- Questions on **online evaluation** test our awareness of production concerns; we should mention metrics like latency, prediction confidence, user engagement, and drift monitoring.
- For **production issues**, strong answers highlight risks like distribution shift, data pipeline failures, or poor handling of unseen edge cases.
- When faced with **data challenges** (e.g., class imbalance), we demonstrate our grasp of solutions like resampling, class weighting, and choosing the right evaluation metrics.
- These follow-up answers are a prime chance to prove our **real-world awareness**, even if our initial model wasn’t perfect—they show we think like engineers, not just coders.

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/>

Paper Reading: ClickHouse - Lightning Fast Analytics for Everyone

Session led by [Dharin Shah](https://www.linkedin.com/in/dharinshah/)

Link to Paper: [**https://www.vldb.org/pvldb/vol17/p3731-schulze.pdf**](https://www.vldb.org/pvldb/vol17/p3731-schulze.pdf)

This paper presents an overview of ClickHouse, a popular opensource OLAP database designed for high-performance analytics over petabyte-scale data sets with high ingestion rates. Its storage layer combines a data format based on traditional log-structured merge (LSM) trees with novel techniques for continuous transformation (e.g. aggregation, archiving) of historical data in the background. Queries are written in a convenient SQL dialect and processed by a state-of-the-art vectorized query execution engine with optional code compilation. ClickHouse makes aggressive use of pruning techniques to avoid evaluating irrelevant data in queries. Other data management systems can be integrated at the table function, table engine, or database engine level. Real-world benchmarks demonstrate that ClickHouse is amongst the fastest analytical databases on the market.\
\
We will go into the details of why and how Clickhouse is one of the fastest OLAP database, its underlying storage engine, and the overall philosophy for performance.

Talk To Engineers

We explain how to get a real, unbiased understanding of a team by talking directly to engineers—not just the hiring manager.

Here are the core points from the lesson:

- We stress that managers have strong incentives to “sell” their team—they’re evaluated on hiring success and growth—so their perspective is inherently biased and overly positive.

- We recommend explicitly asking to meet 1-2 engineers on the team, ideally ICs who have been there for years and are leaving on good terms, as they provide the most honest, nuanced picture of day-to-day culture, challenges, and growth.

- We emphasize that speaking with the tech lead is especially valuable, since alongside the manager, the TL will influence our experience more than anyone else after joining.

What You’ll Learn

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

This course is designed to give you a tactical, insider-level understanding of how to succeed in engineering manager interviews. It focuses on aligning your preparation and responses with what senior leadership is actually looking for in candidates.

- **Understand EM Hiring Criteria:** Learn exactly what directors and senior leaders expect when evaluating EM candidates.
- **Persuasive Interviewing:** Develop strategies to convincingly present yourself to hiring managers at the director level and above.
- **Leadership Storytelling:** Discover which leadership stories to craft and practice to demonstrate your readiness.
- **Answer Optimization:** Learn how to elevate your responses from “good” to “unforgettable” by tailoring them for impact.
- **Practice-Based Learning:** Apply concepts through hands-on exercises to reinforce learning and improve real-time performance.

Choose A Financially Sound Company

We outline the concrete, proactive steps you can take to reduce layoff risk by choosing the right company upfront.

- The most important controllable factor is company selection, with profitability as a key litmus test, since companies that spend more than they earn are far more vulnerable in tighter markets.
- Hiring quality and pace matter: overly easy interviews and hyper-aggressive headcount growth are red flags that often signal overhiring and future layoffs.
- Healthy companies tend to grow steadily rather than stagnate or attempt extreme, unsustainable expansion, especially given the high cost of tech talent.
- Clear product direction and strong leadership are critical, particularly for startups, and can often be evaluated through interviews, vision clarity, and how leaders communicate.
- There are two valid ways to assess company health—analyzing financials directly or reading secondary signals like employee quality, departures, and trusted insider perspectives—and combining both approaches gives the strongest signal.

Breaking Down Startups

We break down what it really means to join a VC-backed, high-growth startup and how the experience differs from big tech.

Here are the core points from the lesson:

- We outline the major upsides of early-stage startups: massive technical scope, the highest possible career ceiling, and deep fulfillment from directly shaping a company’s destiny—especially when joining at Series B or earlier.
- We explain the risks: early startups are unstable, resource-constrained, and extremely high-pressure, where production issues can threaten the company’s survival and where chaos is normal, not exceptional.
- We show why growth is the single most important signal when evaluating a startup: growth increases technical complexity, headcount, leadership opportunities, and the likelihood of exceptional financial upside; stagnation is a strong red flag.
- We contrast this with agencies/consultancies, noting they rarely offer strong long-term growth for early-career engineers unless someone is already a world-class expert—making startup or big-tech paths much more reliable career foundations.

Technical Bar

If you want direct interview support from Alex and Uzair, take their live Maven course with discount code **TARO10**: <https://maven.com/alex-kliotzkin/acing-em-interviews>

This lesson breaks down how **technical competence** is evaluated during engineering manager (EM) interviews. Although only 20% of the hiring criteria is based on technical skills, understanding how it's assessed is essential for preparation.

- **High-level technical evaluation:** EM interviews typically assess architecture and system-level thinking rather than deep implementation. The goal is to understand if you grasp the *impact and trade-offs* of technical decisions.
- **System design is critical:** Nearly all companies include a **system design round**, as it's a strong signal of your ability to reason about architecture, scalability, and trade-offs at a strategic level.
- **Operational excellence is a focus:** Interviewers may evaluate how you ensure **continuous product integrity** through CI/CD pipelines, quality control, monitoring systems, and delivery processes.
- **Metrics and impact measurement:** Expect questions around **KPIs and OKRs**, especially on how you track team or product success. This reflects your business awareness and leadership maturity.
- **Coding expectations vary by company:** FAANG companies often include **coding rounds** to assess whether you can "get your hands dirty" if needed. Non-FAANG companies may skip this or offer take-home assignments or lightweight coding exercises instead.

Taro Book Club: The Pragmatic Programmer - Session 4

Session led by: [Bikram Kashyap](https://www.linkedin.com/in/kashyapbikram/)

In this #taro-bookclub discussion, we'll further go over [**"The Pragmatic Programmer"**](https://www.amazon.com/Pragmatic-Programmer-Journeyman-Master/dp/020161622X) by Andrew Hund & David Thomas.

Here's the agenda:

- Power of Plain Text
- Shell Games
- Power Editing
- Version Control
- Debugging
- Text Manipulation
- Engineering Daybooks

Please send your questions ahead of time to [**Bikram Kashyap**](https://www.linkedin.com/in/kashyapbikram/) 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***](https://taropremium.slack.com/archives/C05CKBHPNTU/p1723491587867879) *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!

Paper Reading Session: Simple Testing Can Prevent Most Critical Failures

In this session, we will be discussing this paper titled "[**Simple Testing Can Prevent Most Critical Failures: An Analysis of Production Failures in Distributed Data-Intensive Systems**](https://www.usenix.org/system/files/conference/osdi14/osdi14-paper-yuan.pdf)".

Large, production quality distributed systems still fail periodically, and do so sometimes catastrophically, where most or all users experience an outage or data loss. This paper presents the result of a comprehensive study investigating 198 randomly selected, user-reported failures that occurred on Cassandra, HBase, Hadoop Distributed File System (HDFS), Hadoop MapReduce, and Redis, with the goal of understanding how one or multiple faults eventually evolve into a user-visible failure and how simple testing can prevent majority of them.

Presented by [Sudhi Awasthi](https://www.jointaro.com/p/sudhi-awasthi/), an engineer working at Bloomberg. Connect with her on LinkedIn: <https://www.linkedin.com/in/sudhiawasthi/>.

Round Structure ML Behavioral

Here’s a summary of what we should expect and aim to convey in the **Project Deep Dive and Behavioral Interview Round**—a conversational but highly valuable part of the ML interview process.

- We’re expected to go beyond surface-level descriptions and **deeply explain a past ML project**, including the problem, our solution, and the technical trade-offs we made.
- This is our chance to show **real-world impact**, how we debugged challenges, optimized performance, or iterated on feedback.
- For **ML infrastructure roles**, we should be ready to discuss tools or systems we built (e.g. training pipelines, feature stores), and how they enabled or scaled team productivity.
- Strong performance in this round depends on **clarity, structure, and storytelling**—connecting technical depth with our thought process and decision-making.
- Ultimately, it’s about showing that we not only *built* things, but thought critically and communicated effectively through the process.

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/>

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.