Amazon Behavioral Interview [Part 33] - Conclusion

This video segment concludes an online course on behavioral interviewing.

- **Key Takeaways:**
  - **Practice is Crucial:** The speakers emphasize the importance of consistent practice, even if it means participating in interviews for jobs you are not necessarily interested in.
  - **Seek Feedback:** The speakers encourage viewers to record themselves practicing interview answers and seek feedback from others.
  - **Leverage the Course Content:** The speakers highlight the value of the course content, emphasizing that similar information would typically cost significantly more from a professional career coach.
  - **Take Action:** The speakers encourage viewers to apply the learned techniques to their own job search and strive for continuous improvement in their interview skills.
  - **Confidence Building:** The speakers aim to empower viewers by emphasizing that with consistent practice and the application of the course's principles, they can significantly improve their interview performance and increase their chances of landing their desired jobs.

This summary highlights the key takeaways from the video, emphasizing the importance of practice, feedback, and continuous learning in the job search process.

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

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.

Breaking Down Big Tech

We outline the real benefits and drawbacks of joining big tech, focusing on what actually matters for long-term career growth rather than the hype.

Here are the core points from the lesson:

- We explain that the biggest advantage of big tech is prestige. Having Google, Meta, Amazon, or Microsoft on our résumé dramatically increases future job opportunities, recruiter response rates, and long-term career mobility.
- We highlight additional strengths: top-tier compensation, strong and reliable learning environments, and overall consistency in job security, interview success, and career progress, all of which compound after a few years inside a major tech company.
- We walk through key drawbacks: massive company size makes it hard to find meaningful scope, many hard problems are already solved, work-life balance can be demanding, and advancement requires strong communication and leadership skills—not just coding ability.
- We stress that big tech isn’t ideal for everyone; engineers who prefer deep, heads-down coding or highly autonomous environments may struggle in the bureaucracy and stakeholder-heavy nature of these organizations.

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.

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.

Level-Specific Expectations

Here’s a summary of what interviewers expect from us in **ML system design interviews** based on role level. As we progress from junior to staff, expectations shift from correctness to ownership to strategic leadership.

- **At the junior level**, we’re expected to understand core ML system components (like feature processing and feedback loops), identify common challenges (e.g., data freshness, label leakage), and demonstrate clarity and correctness in design thinking.
- **At the senior level**, we should drive end-to-end design, justify architectural decisions, evaluate trade-offs (e.g., accuracy vs. latency), proactively address failure modes, and consider long-term system maintenance and monitoring.
- **At the staff level**, we must demonstrate systems thinking across modeling, infra, and product; anticipate edge cases; quantify performance at scale; lead team effort allocation; and address domain-specific complexities like regulations or safety.

By aligning our depth of thinking and communication style with our role level, we show interviewers that we’re ready to operate—and lead—at the right scope.

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/>

Final Thoughts

Congratulations on making it to the final lesson of this course! Simply by reaching this point, you have already set yourself apart as a motivated and proactive machine learning candidate.

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/>

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.

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.

Optimize Your Performance

We focus on the most practical actions within your control to reduce layoff risk by becoming a consistently high-performing, high-impact contributor.

- Optimizing performance should be a constant priority, starting with deeply understanding your company’s performance review system and knowing exactly what ratings mean and how they affect your risk and growth.
- Staying calm and avoiding gossip or obsession with layoff rumors is critical, since anxiety and distraction directly undermine performance and effectiveness.
- Maintaining a clear, transparent, and concise dialogue with your manager is essential, because managers advocate for you using only a few high-level impact points.
- Expanding scope and proactively tackling important, ambiguous problems is one of the strongest career multipliers and a key trait of top performers.
- Building a clear professional “brand” on your team—where others can easily articulate what you’re known for and the value you deliver—makes you more visible, promotable, and resilient during layoffs.

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.

Code Quality

In this section, we focus on **code quality**, a crucial yet often underestimated element of ML interviews. Even if our model works well, poorly written code can seriously damage our impression. Clean, clear code reflects professional maturity and real-world readiness.

- **Mediocre code** may be functional but is hard to read—characterized by cryptic variable names, long repetitive functions, unclear logic, and little to no documentation.
- **Stellar candidates write clean, organized code**, breaking it into logical chunks with reusable functions and clearly labeled sections.
- We use **meaningful variable names**, write comments that explain the "why" behind decisions, and maintain a logical flow from start to finish.
- Our code is **efficient in design**, avoiding unnecessary complexity and maintaining smooth execution without over-optimizing.
- Ultimately, we recognize that **code is communication**—and polished, maintainable code signals we’re ready to contribute to real engineering teams.

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: AlphaGeometry: Advancing Automated Reasoning in Olympiad-Level Geometry

Paper Discussion led by [Lucia Mocz](https://www.linkedin.com/in/lucia-mocz-ph-d-35a98a1b6/)

Solving mathematical problems at the Olympiad level is incredibly difficult, requiring advanced problem-solving skills, a deep understanding of mathematical principles, and the ability to think critically and creatively under pressure. Those who excel in Olympiad-level problem solving usually have exceptional talent, undergo extensive preparation, and possess a unique problem-solving mindset. Achieving human-level automated reasoning in mathematical theorem proving, especially when competing with top performers, is a significant milestone in artificial intelligence research.

In this paper reading session, we introduce AlphaGeometry, a groundbreaking theorem prover tailored for Euclidean plane geometry. Unlike traditional methods that rely on human demonstrations, AlphaGeometry autonomously synthesizes millions of theorems and proofs across various complexities without human intervention. By combining a neural language model trained on vast synthetic data with a symbolic deduction engine, AlphaGeometry employs a neuro-symbolic approach to navigate intricate problem spaces. Notably, AlphaGeometry generates human-readable proofs and has successfully tackled challenging problems from previous International Mathematical Olympiads, even discovering new generalizations of established theorems.

Whether you're an AI researcher, engineer, or simply curious about this achievement in AI research, join us as we explore the intricacies of AlphaGeometry and its potential implications for automated reasoning in mathematics.\
\
Paper: [**https://www.nature.com/articles/s41586-023-06747-5**](https://www.nature.com/articles/s41586-023-06747-5)\
Event Link: <https://www.jointaro.com/event/paper-reading-alphageometry-advancing-automated-reasoning-in-olympiad-level-geometry/>

Blog: [**https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/**](https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/)

GitHub: [**https://github.com/google-deepmind/alphageometry**](https://github.com/google-deepmind/alphageometry)

Learnings from SWE to Senior SWE at LinkedIn

*If you're not part of Taro Premium, join with Paras's link to receive a discount: [**https://www.jointaro.com/r/parass876/**](https://www.jointaro.com/r/parass876/)*

Jumping into Software Engineering from a non-technical background can be challenging, but it’s far from impossible. Join us for an inspiring session with [**Paras Sidhu**](https://www.linkedin.com/in/paras-sidhu/), who not only transitioned from mathematics into software engineering, but also cracked big tech in just 2 years and earned a promotion to Senior Software Engineer within another 2 years.

### **What to Expect**

1. **Actionable Tips**: Paras will share his experiences and actionable tips on his journey from Software Engineer to Senior Software Engineer, providing practical examples that you can apply to your own career path.
2. **Insightful Learnings**: Gain valuable insights into how to navigate and excel in big tech, even if you come from a non-technical background.
3. **Q&A Session**: Don’t miss the chance to ask your burning questions and get personalized advice from someone who has successfully made the transition and accelerated his career.

Mark your calendars and prepare to be inspired! Don’t miss out on this chance to learn from Paras' unique journey and gather tips that could propel your own career forward.

Looking forward to seeing you there!

What Engineers Get Wrong About Open Source (From the OSS Lead at Meta)

Open source software is not just about the code! It's about understanding and working with project maintainers to solve a problem.

The STAR+R Method For ML Experiences

In this segment, we explore how to present our machine learning project during a behavioral interview using an enhanced **STAR** method—with an extra **R** for **Reflection**. This approach helps us communicate our work clearly and effectively while demonstrating technical depth and self-awareness.

- **S – Situation**: We start by setting the context—what was the problem, when did it occur, and what were the constraints (e.g., latency limits, scale requirements, integration needs).
- **T – Task**: We define our role and specific goals. For example, improving a ranking model to boost session engagement by a measurable target.
- **A – Action**: We go deep on what we actually built. This includes model architecture choices (e.g., deep neural networks, two-tower systems), feature engineering, and design trade-offs. We explain why certain techniques were selected and how they fit into the production stack.
- **R – Result**: We share the measurable impact of our work—like a 2.2% engagement lift—and tie it to broader business or platform outcomes (e.g., full rollout after a successful A/B test).
- **R – Reflection**: Finally, we add a layer of introspection, discussing what we learned or would do differently next time—showing growth, humility, and systems thinking.

This STAR+R framework not only helps structure our storytelling but also leaves a lasting impression of us as thoughtful, impact-oriented ML professionals.

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: LLaMA  Open and Efficient Foundation Language Models

Session Led By: [Mike A](https://www.linkedin.com/in/malik-mike-a-32b63818b/)

Join us for the first installment of our three-part paper reading series on the LLaMA series of Large Language Models (LLMs).\
\
In this session, we will explore the foundational concepts and innovations behind the initially released LLaMA models, introduced by Meta AI back in early 2023. The first series of LLaMA models, ranging from 7B to 65B parameters, and how they opened the door to efficient, open-weight LLMs that can be trained on publicly available datasets.

This event will cover the groundbreaking at the time techniques used to train LLaMA models, which outperformed previous state-of-the-art models like GPT-3 on many benchmarks, despite being significantly smaller. We'll focus on the details of their unique training approach, data mixture, and architectural enhancements. We will also discuss the impressive zero-shot and few-shot capabilities of these models on tasks, highlighting the efficiency and accessibility that made these models popular within the open-source community.

This session is a must-attend for researchers, developers, and AI enthusiasts interested in the recent history behind the latest advancements in language modeling and AI research as we build our understanding of the LLaMA models towards the latest releases from Meta AI. Whether you're keen to understand the intricacies of LLMs, explore the potential of open-source AI models, or gain insights into the future of efficient AI by reflecting on the past, this paper reading will provide valuable knowledge and stimulate thought-provoking considerations.

Mark your calendars for an enlightening hour of "LLaMA: Open and Efficient Foundation Language Models" and be part of the journey to explore the future of AI language models through a review of the past.

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.

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.