Amazon Behavioral Interview [Part 16] - Going Above and Beyond For A Customer (Evaluation)

This video segment provides feedback on an individual's response to a behavioral interview question: "Tell me about a time when you went above and beyond for a customer."

- **Strengths:**

  - **Impactful Story:** The interviewee described a compelling situation where they went above and beyond to help a customer who was facing a significant issue with their credit card insurance.
  - **Demonstrated Initiative:** They proactively investigated the issue, identified a potential resolution, and engaged with key stakeholders, including a senior executive, to advocate for the customer.
  - **Highlighted Positive Outcomes:** The interviewee emphasized the positive outcomes of their actions, including:
    - Resolving the customer's issue and restoring their satisfaction.
    - Retaining a valuable customer who was considering canceling their card.
    - Driving a policy change to improve the customer experience for future cardholders.

- **Areas for Improvement:**

  - **Further Emphasize Leadership and Influence:** While the interviewee mentioned engaging with the SVP, they could have further emphasized their leadership and influence by describing how they:
    - Presented a compelling case for the customer's situation.
    - Overcame potential objections from the SVP.
    - Effectively communicated the importance of this issue to senior leadership.
  - **Quantify Impact Further:** While the interviewee mentioned the customer's annual spending, they could have further quantified the impact of their actions by:
    - Estimating the potential lifetime value of the customer.
    - Describing the positive impact on customer satisfaction scores.
    - Highlighting the potential long-term benefits of the policy change.

- **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 customer-centric 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).

Become An Engineer Influencer [Part 16] - Editing

Become An Engineer Influencer [Part 25] - Digital Products

Evaluation

In this section, we explore **model evaluation**, a phase that’s often underestimated but critically important. This is our chance to show we’re not just optimizing metrics—we’re aligning our work with real-world impact.

- **Mediocre evaluation** involves simply calling `.score()` or reporting accuracy, with little explanation or context—a major red flag in interviews.
- **Stellar candidates choose metrics that fit the problem**, such as F1 score or AUC-ROC for imbalanced classification, or MAE over RMSE for certain regression tasks.
- We demonstrate awareness of **trade-offs**, like how boosting recall might reduce precision, and explain the implications clearly.
- Most importantly, we **tie evaluation to business goals**, showing we understand the cost of errors and what metrics matter most in practice.
- This thoughtful approach proves that we’re not just building models—we’re optimizing for meaningful outcomes.

If you want to learn even more from Yayun:

- Book 1 on 1 career guidance with her here: <https://calendly.com/your-friend-yayun/1on1>
- Follow her on LinkedIn: <https://www.linkedin.com/in/yayunjin/>

Become An Engineer Influencer [Part 22] - Monetization Strategies

Notebook Interview Preparation Tips

In this final segment, we shift from execution to preparation—learning how to practice effectively for notebook-style ML interviews. The focus is on developing real-world fluency, structured habits, and clear communication.

- We should **practice with real, messy datasets** from platforms like Kaggle or UCI to simulate the open-ended nature of actual interview problems.
- We’re encouraged to build a **reusable workflow template**—a consistent structure (e.g., data loading, EDA, feature engineering, modeling, evaluation)—to streamline our process and reduce cognitive load.
- We must get used to **explaining our decisions out loud**, as live interviews often involve narrating our thought process clearly and confidently.
- Throughout, we should **document our work as we go**, making it easier for interviewers to follow our logic and showing that we value clarity.
- Most importantly, we’re reminded to **think beyond accuracy**—considering the user impact, business value, and maintainability of our solution.
- A bonus tip: **practice talking while coding** to improve our ability to communicate effectively under real-time conditions. This small habit can make a big difference in live interviews.

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 24] - Sacrificing Short-Term For Long-Term (Evaluation)

This video segment provides feedback on an individual's response to a behavioral interview question about a time they sacrificed short-term gains for long-term value.

- **Strengths:** The interviewee effectively described the initial situation, highlighting the inefficiencies of the existing regional team structure and the need for a more globally aligned approach.

- **Areas for Improvement:**

  - **Focus on Long-Term Impact:** The interviewee could have more clearly and quantitatively demonstrated the long-term positive impact of the changes, such as specific improvements in key performance indicators (KPIs) beyond just reduced resolution times.
  - **Minimize Negative Framing:** The interviewee could have reframed their description of the challenges faced, minimizing the emphasis on negative aspects like "disgruntled people" and "pain points."
  - **Demonstrate Stronger Leadership:** While the interviewee mentioned gathering feedback and collaborating with stakeholders, they could have further emphasized their leadership skills, such as:
    - Proactively communicating the vision and rationale for the change to the team.
    - Effectively managing stakeholder expectations and addressing concerns.
    - Motivating and empowering the team throughout the transition process.

- **Overall:** While the interviewee effectively described the situation and the challenges faced, the response could be strengthened by:

  - **Focusing on positive outcomes:** Emphasizing the significant improvements in efficiency, customer satisfaction, and team performance.
  - **Demonstrating stronger leadership and communication skills:** Highlighting proactive measures taken to address team concerns and ensure successful implementation.
  - **Minimizing negative framing:** Presenting the challenges in a more constructive and solution-oriented manner.

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

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/>

Maximize Your Equity And Benefits In Tech [Part 15] - Alternative Minimum Tax Basics

Alternative Minimum Tax (AMT) can create surprise tax liabilities, especially when exercising incentive stock options. Here's the core points from the lesson:

- AMT is a parallel tax system with fewer deductions, aimed at ensuring high earners pay baseline taxes, and it most often affects those exercising ISOs below market value
- Unlike the progressive regular tax system, AMT uses flat rates (26% or 28%) and doesn’t allow deductions like standard or state/local taxes, leading to potentially higher taxes
- The difference between ISO strike price and market value counts as AMT income, which may trigger a large tax bill even if the shares aren't sold
- Any excess AMT paid over regular tax becomes a credit that can offset future tax liabilities, but recovering it depends on your future income and tax situation

[Check out my AMT Calculator here](https://adastrafp.com/amt-calculator/) (for informational purposes only, not tax advice.)

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/)

Don't Freak Out

We emphasize the mindset shift that matters most when facing the reality of layoffs and uncertainty in tech.

- The most important first step is not to freak out, even though layoffs can feel shocking and destabilizing, especially for people who assumed big tech jobs were inherently safe.
- Acceptance is key: layoffs are a normal part of professional life, often driven by factors far outside an individual contributor’s control, including executive decisions and market conditions.
- Obsessing over layoffs or drastically changing your behavior can actually backfire, while staying steady and focused on your work is usually the healthier and more effective approach.
- If a layoff is going to happen, the decision is typically already made, so recognizing what you cannot control can be freeing and allows you to focus your energy on what you actually can control.

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/>

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).

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/>

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!

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)

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.

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.

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.

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/>

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.

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.