Amazon Behavioral Interview [Part 32] - Missing A Commitment (Evaluation)

This video segment provides feedback on an individual's response to a behavioral interview question about a time they failed to meet a commitment.

- **Strengths:**

  - **Relevant and Engaging Story:** The interviewee provided a compelling and relevant example by describing a critical incident during the Super Bowl, a high-pressure situation.
  - **Honest Acknowledgement of Failure:** The interviewee openly acknowledged the failure and took responsibility for the issue.
  - **Detailed Explanation:** The interviewee provided a detailed explanation of the situation, including the specific challenges encountered (e.g., software bug, communication issues), the impact of the failure, and the steps taken to address the issue.
  - **Focus on Learning and Improvement:** The interviewee highlighted the key learnings from the experience, such as the importance of thorough testing, robust contingency plans, and clear escalation procedures.
  - **Positive Outcome:** The interviewee emphasized that the team ultimately resolved the issue and learned valuable lessons, demonstrating their ability to learn and adapt.

- **Areas for Potential Improvement:**

  - **Deeper Dive into Root Cause Analysis:** The interviewee could have provided more detail on the specific steps taken to identify the root cause of the software bug and how the team prevented similar issues from occurring in the future.
  - **Highlighting Leadership:** The interviewee could have further emphasized their leadership role in addressing the situation, such as:
    - Taking ownership of the problem and communicating proactively with stakeholders.
    - Motivating the team to work together to find a solution.
    - Effectively communicating the resolution to all stakeholders.

- **Overall:**

  - This was a strong response that effectively demonstrated the interviewee's ability to:
    - Acknowledge and learn from mistakes.
    - Take responsibility for their actions.
    - Demonstrate problem-solving and critical thinking skills.
    - Highlight their ability to work effectively under pressure and as part of a team.
    - Communicate their experiences clearly and concisely.

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

Amazon Behavioral Interview [Part 14] - Delivering Results (Evaluation)

This video provides feedback on an individual's response to a behavioral interview question.

- **Strengths:**

  - **Strong Storytelling:** The interviewee effectively set the scene, highlighting the chaotic situation with multiple communication channels and the potential for critical issues to slip through the cracks.
  - **Clear and Concise Solution:** They clearly outlined their solution: creating a single point of entry for all escalations.
  - **Demonstrated Collaboration:** The interviewee emphasized the importance of cross-functional collaboration, highlighting their efforts to gain buy-in from stakeholders, including the Chief Revenue Officer (CRO).
  - **Acknowledged Challenges:** The interviewee acknowledged the challenges they faced, such as initial resistance from some stakeholders and the need to address concerns about the new process.
  - **Quantifiable Results:** They demonstrated the success of their solution by highlighting the significant increase in compliance with the new process.

- **Areas for Improvement:**

  - **Quantify Impact Further:** While the interviewee mentioned increased compliance, they could have further quantified their impact by mentioning specific metrics, such as a reduction in the number of unresolved escalations, improved customer satisfaction scores, or cost savings associated with the improved efficiency.
  - **Highlight Leadership and Ownership:** While the interviewee acknowledged the support of their leadership, they could have further emphasized their own leadership and initiative in driving the project forward.
  - **Address Potential Concerns Proactively:** The interviewee could have proactively addressed potential concerns, such as the initial resistance from some stakeholders, and outlined the steps they took to mitigate these concerns.

- **Overall:** The interviewee provided a strong foundation for their response. By incorporating the suggested improvements, they can further enhance their ability to effectively communicate their accomplishments and demonstrate their leadership, problem-solving, and results-oriented approach in a job interview setting.

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

How Managers Hire Great Engineers [Part 15] - Initial Contact

You only get one first impression, so you must make that initial outreach message count. From this lesson, you will:

- Discover how to craft an engaging subject line and personalize the content to stand out from typical recruiter communications.
- Understand the importance of keeping the email concise, with a clear, brief point that invites the recipient to engage further.
- Learn optimal timing for sending emails to maximize response rates, avoiding busy times like early Monday or late Friday.

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

Amazon Behavioral Interview [Part 15] - Going Above and Beyond For A Customer (Answer)

This video segment describes a situation where the interviewee went above and beyond to help a dissatisfied customer.

- **Identified a Customer Issue:** The interviewee encountered a platinum card customer who was extremely upset because their car, which was damaged while skiing, was not covered by the card's rental insurance due to a specific exclusion in the terms and conditions.
- **Went Above and Beyond:** Despite the company policy, the interviewee recognized the customer's frustration and proactively sought a resolution. They:
  - Investigated the issue and identified the relevant policy.
  - Engaged with internal stakeholders, including the product team and a senior executive, to advocate for the customer.
  - Successfully secured approval for a $4,000 statement credit to cover the repair costs.
  - Further impressed the customer by arranging for a check to be mailed directly, providing a more tangible and personalized gesture of goodwill.
- **Achieved Positive Outcomes:**
  - The customer was highly satisfied with the resolution and even became a testimonial for the company.
  - The company retained a valuable customer who was initially considering canceling their card.
  - The incident led to a review of the policy and improved communication regarding the scope of rental car insurance coverage.

This example demonstrates the interviewee's commitment to customer satisfaction, their ability to navigate internal processes, and their proactive approach to resolving customer issues.

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

Notebook Interview Example: Ad Click Prediction

In this final part of the section, we bring all the core concepts together through a realistic, end-to-end ML interview example. The walkthrough demonstrates how to apply best practices across the entire notebook round—from EDA to model evaluation—with clarity, intention, and real-world relevance.

- In **EDA**, we go beyond surface checks by exploring unique value counts, visualizing target distribution, spotting class imbalance, and identifying trends like CTR by device type—ensuring every insight informs downstream modeling.
- In **feature engineering**, we thoughtfully create new features (e.g., age buckets, time-of-day flags), handle multicollinearity, and tailor transformations based on domain intuition, not just standard recipes.
- During **model training**, we justify our choice of random forest for its robustness and interpretability, apply cross-validation and stratified splits, and run grid search for tuning—demonstrating intention behind every step.
- In **evaluation**, we use metrics that align with the problem (precision, recall, F1, AUC-ROC, confusion matrix) and interpret results in context, showing we understand the real-world stakes of false positives and negatives.
- For **follow-up questions**, we proactively discuss improvements (e.g., deeper models, richer features), production monitoring (CTR by segment, drift detection), and handling class imbalance—proving we think like end-to-end ML practitioners, not just coders.

This example illustrates how to move from “checking boxes” to demonstrating thoughtful, production-aware problem-solving under real interview conditions.

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/>

Your Rating Should Never Be A Surprise

In this lesson, we explain why your performance rating should *never* be a negative surprise and how maintaining transparent, ongoing conversations with your EM prevents career-derailing outcomes.

- We emphasize that if you expect “meets” and end up on a PIP, something has gone catastrophically wrong—and it’s almost always preventable through proactive communication.
- We recommend regularly asking for a *hypothetical rating* (“If you had to rate me today, what would it be?”) while explicitly acknowledging it’s not a guarantee—this makes managers comfortable giving candid signals.
- We highlight that good managers are usually 80–90% accurate with these hypothetical ratings, giving you strong insight into where you truly stand.
- We note that engineers who avoid this conversation out of discomfort effectively “walk themselves off a cliff” by choosing to stay in the dark.
- We reinforce that building trust through vulnerability, consistency, and strong 1:1 habits unlocks your manager’s willingness to give honest performance signals—ensuring you’re never blindsided.

ML Algorithm Example: K-means

In this segment, we walk through a high-level structure for implementing **k-means clustering from scratch**, focusing on clarity, modularity, and real-time reasoning. The emphasis is not on memorizing code, but on understanding the logic and being able to build it confidently.

- We start by defining an `__init__` method to store **core parameters** like the number of clusters (k) and max iterations.
- We **initialize centroids** by randomly selecting k data points; interviewers may also ask about smarter strategies like **k-means++**.
- In the **assign_clusters** step, we calculate distances from each point to every centroid and assign points to the nearest one.
- The **update_centroids** method recomputes each centroid as the mean of its assigned points; optionally, we may include a **loss function** to track clustering quality.
- The **fit** method loops through initialization, assignment, and updates until convergence, while **predict** assigns clusters to new data after training.
- Most importantly, we aim for **clean, modular, and well-structured code**, and narrate our logic during the interview to demonstrate deep understanding—not just syntax.

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/>

Amazon Behavioral Interview [Part 28] - Recovering From A Bad Decision (Evaluation)

This video segment provides feedback on an individual's response to a behavioral interview question about a time they made a bad decision.

- **Strengths:**

  - **Owned the Mistake:** The interviewee openly acknowledged the mistake and took responsibility for the negative consequences.
  - **Demonstrated Self-Awareness:** They identified the root cause of the mistake – focusing solely on short-term revenue gains while neglecting long-term customer value and potential.
  - **Highlighted Corrective Actions:** The interviewee described the steps taken to rectify the situation, such as revising the incentive structure, improving call center response times, and implementing measures to address long wait times.
  - **Learned from the Experience:** The interviewee outlined the key learnings from this experience, including the importance of considering long-term customer value and gathering input from stakeholders before implementing significant changes.

- **Areas for Improvement:**

  - **Quantify Impact:** The interviewee could have further quantified the impact of the mistake and the subsequent corrective actions. For example, they could have provided specific data on the improvement in customer satisfaction scores or the increase in customer retention rates after implementing the changes.
  - **Emphasize Positive Outcomes:** The interviewee could have further emphasized the positive outcomes of the project, such as the improved focus on customer lifetime value and the development of a more robust customer retention strategy.
  - **Connect to Amazon Leadership Principles:** The interviewee could have explicitly connected their actions and learnings to relevant Amazon Leadership Principles, such as "Customer Obsession," "Learn and Be Curious," and "Ownership."

- **Overall:**

  - The interviewee effectively demonstrated their ability to acknowledge and learn from mistakes, take responsibility for their actions, and implement corrective measures.
  - By incorporating the suggested improvements, the interviewee can further enhance their response and increase their chances of success in the interview.

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

Framing The Dialog

We cover how promotions actually work, why they’re not guaranteed even if we’re performing well, and how to talk about them with our EM in a way that aligns incentives and builds trust.

- We explain that promotions require **both** performing at the next level **and** an organizational need for someone at that level—otherwise even great performers can’t move up.
- We note that promotion discussions require trust; bringing up promotions too early (e.g., in week one) backfires because the manager doesn’t yet know our work or commitment to the team.
- We warn that reorgs can derail promotion momentum when a new manager must advocate for us without shared history; coordinating between old and new managers helps mitigate this.
- We highlight that promotions are meant to signal that we already operate with higher scope, independence, and cross-org trust—not something to wish into existence.
- We emphasize framing promotion goals in terms of **increasing team impact** rather than title or compensation, since managers (especially in tight-budget environments) prioritize engineers who help them deliver more impact, not those who simply want advancement.

EM vs. IC

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 breaks down the fundamental differences between the roles of individual contributors (ICs) and engineering managers (EMs), using a structured view to clarify the contrast in scope, responsibility, and domain expertise. It emphasizes how transitioning from IC to EM is a **career shift**, not a promotion, due to the shift in focus from implementation to strategy and people leadership.

- **ICs focus deeply on implementation and domain expertise**, such as full-stack, cloud, embedded, or AI systems. They are vertical specialists with strong knowledge in their technical area.
- **ICs also operate within the software engineering realm**, applying practices like CI/CD, agile processes, and architecture—but mainly to support their implementation work.
- **EMs must be experts in software engineering best practices**, not just to implement them but to ensure sustainable development and product integrity across the team.
- **The core domain of EMs is technical leadership**, which includes stakeholder management, translating business needs into technical goals, strategic planning, communication, and team mentorship (i.e. areas ICs typically don't engage with).
- **Managing both delivery and team dynamics** makes the EM role distinctly broader, requiring versatility across both technical and people-focused domains.

Become An Engineer Influencer [Part 21] - Networking

Become An Engineer Influencer [Part 19] - What If Nothing Works?

What Are Advanced Concepts I Should Know?

In this lesson we talk about how to effectively prepare for AI-assisted coding interviews, with a focus on shifting your study habits to match what these interviews actually test.

- **Learn the "why" before the "how"** — rather than drilling implementations, focus on understanding the canonical problem each data structure solves and practice explaining it in one sentence out loud; with AI handling the code, being unable to independently identify the right approach is now much harder to hide or compensate for.
- **Know the common gotchas for each pattern** — every classic structure has a specific edge case trap (e.g. the `is_end` flag in a trie, recency updates in an LRU cache, off-by-one errors in sliding windows); getting familiar with these upfront dramatically speeds up your ability to verify AI-generated output during the interview.
- **Practice reading code, not just writing it** — since interviews now involve more time spent reading starter code and verifying AI output than typing from scratch, deliberately train yourself to mentally trace logic quickly by reading existing solutions online or having AI generate code for problems you already understand, then tracing through them by hand.

Why Coding Matters In ML

In this new section on **coding fundamentals**, we explore why strong programming skills are essential for machine learning engineers. While ML involves data and models, it’s also deeply rooted in real-world software engineering.

- We clarify a common myth: **ML engineers are still engineers**, and are expected to meet the same coding standards as traditional software engineers.
- Our **software engineering background is an asset**—if applied correctly, it can set us apart from more research-focused ML candidates.
- **Coding matters in ML** because models must be integrated into production systems, maintained, and optimized for performance and scalability.
- We’re reminded to consider **algorithmic complexity**, especially when building pipelines or serving models at scale.
- The key takeaway: **don’t underestimate LeetCode-style coding prep**. Many ML interviews, especially at top tech companies, still include traditional algorithm and data structure questions. Strong coding skills are non-negotiable.

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/>

Amazon Behavioral Interview [Part 29] - Building Trust With Stakeholders (Answer)

This video segment focuses on how to build trust relationships with colleagues and stakeholders.

- **Key Strategies:**

  - **Proactive Relationship Building:**
    - The interviewee emphasizes the importance of proactive relationship building by reaching out to stakeholders early on, even before needing to rely on them. This includes conducting preliminary research, scheduling introductory meetings, and taking the time to understand their individual goals and priorities.
  - **Demonstrating Integrity and Consistency:**
    - The interviewee highlights the importance of acting with integrity, following through on commitments, and being transparent about their goals and priorities. This builds trust and credibility with colleagues.
  - **Assuming Trust:**
    - While acknowledging the importance of earning trust, the interviewee believes in starting with a foundation of trust and assuming positive intent from others.
  - **Open and Honest Communication:**
    - The interviewee emphasizes the importance of open and honest communication, including being transparent about priorities and setting clear expectations.

- **Practical Tips:**

  - **Leverage tools like "The First 90 Days" to effectively identify and map stakeholders.**
  - **Conduct introductory meetings with key stakeholders to build rapport and understand their perspectives.**
  - **Focus on building relationships based on mutual respect and trust.**
  - **Prioritize consistency and follow-through on commitments.**
  - **Communicate openly and honestly about goals and priorities.**

This approach to building trust relationships emphasizes proactiveness, integrity, open communication, and a focus on understanding and meeting the needs of stakeholders.

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

Become An Engineer Influencer [Part 24] - Consulting

Feedback Is A Gift. Seriously

In this lesson, we explore how feedback, especially how we *receive* it, is one of the most important levers for building trust with our engineering manager and accelerating our career growth.

- We highlight that managers are the primary (and often only) source of meaningful feedback, since peers rarely want the discomfort or risk of giving constructive criticism.
- We emphasize that when we first receive feedback, our job is to say **thank you**, avoid defensiveness, and take time to reflect—even if we disagree or feel misunderstood.
- We explain that most feedback contains at least a kernel of truth, and our responsibility is to identify what’s useful and implement changes quickly, then follow up later to show progress.
- We warn that pushing back defensively can permanently shut down future feedback, leaving us “rudderless” with no insight into what to improve.
- We share that the fastest-growing engineers treat feedback as a superpower: they assume good intent, absorb nuance, implement rapidly, and create a virtuous cycle where their manager is eager to invest even more in their growth.

Hiring Manager (Director+) Perspective

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 of the course explains why understanding the mindset of the engineering manager (EM) hiring manager—typically a director or above—is critical for succeeding in EM interviews. It emphasizes that trust, not just competence, is the cornerstone of what these senior leaders are evaluating.

- **Trust is the foundation of EM hiring.** Directors operate in a world filled with ambiguity—changing priorities, unclear requirements, and frequent people challenges. They need leaders they can trust implicitly.
- **Competence is not enough.** While technical ability is important, directors are evaluating whether a candidate will help solve problems or become another issue that needs managing.
- **Reliability during adversity is crucial.** Senior leaders are asking: *Will this person make my life easier when things get tough?* Candidates must show they can thrive in uncertainty and still deliver results.
- **The hiring lens is risk-focused.** From the director’s perspective, hiring an EM is about minimizing risk. The ideal EM is a stabilizing force—someone who says, *“Trust me, I’ve got this”*—and actually means it.

Amazon Behavioral Interview [Part 26] - Working Outside Of Your Domain (Evaluation)

This video segment analyzes an individual's response to a behavioral interview question about taking on a project outside of their core responsibilities.

- **Strengths:**

  - **Identified a Problem:** The interviewee effectively identified a critical issue: low employee engagement scores regarding promotion fairness.
  - **Took Initiative:** They proactively sought to address this issue by developing and implementing a new feedback system ("Perfolution").
  - **Demonstrated Collaboration:** The interviewee successfully collaborated with HR to implement the system and gather data.

- **Areas for Improvement:**

  - **Quantify Impact:** The interviewee could have more clearly quantified the impact of the "Perfolution" system. Instead of simply stating that scores "went up," they could have provided specific data points, such as the percentage increase in employee satisfaction scores related to promotion fairness.
  - **Focus on Leadership:** The interviewee could have further emphasized their leadership role in driving the project, such as:
    - Motivating their team to participate in the feedback process.
    - Overcoming resistance or challenges encountered during implementation.
    - Communicating the results of the project to key stakeholders and securing buy-in for necessary changes.
  - **Connect to Amazon Leadership Principles:** The interviewee could have explicitly connected their actions to relevant Amazon Leadership Principles, such as "Ownership," "Customer Obsession," and "Bias for Action."

- **Overall:**

  - While the interviewee demonstrated initiative and problem-solving skills, the response could be strengthened by:
    - Providing more specific and quantifiable data on the impact of the project.
    - Further emphasizing their leadership role and the challenges they overcame.
    - Explicitly connecting their actions to relevant Amazon Leadership Principles.

This analysis provides valuable insights into how to effectively communicate accomplishments and demonstrate leadership qualities in a job interview context.

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

Mock Interviews Are Non-Negotiable

To wrap up our preparation strategy, we dive into how to *structure* our prep effectively—because randomly studying “a bit of everything” doesn’t work.

- **Mock interviews are non-negotiable**: They’re the fastest way to sharpen your skills, build confidence, and improve how you communicate under pressure.
- **Avoid practicing in real interviews**: We should use mocks to make mistakes and learn, not high-stakes opportunities.
- **Find partners in your network or online**: Leverage communities or platforms like ADP List to schedule mock sessions with others.
- **Record your mocks**: Watching ourselves afterward reveals blind spots we may not notice in the moment—this step alone can lead to major improvements.

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.