Having The Meeting

In this lesson, we walk through exactly how to run a high-impact 1:1 with our EM by using the time intentionally to build trust, surface wins, work through challenges, and create momentum through clear action items.

- We start each meeting with 2–5 minutes of personal check-in to strengthen rapport, since life and work affect each other deeply.
- We share weekly wins to stay visible and ensure our contributions are recognized—while also making future performance review writing much easier.
- We move through our pre-seeded agenda topics (e.g., promotion planning, feedback digestion, team conflicts), which flow smoothly because we prepared them ahead of time.
- We close with clear action items for both sides—then begin the next week’s 1:1 by reviewing whether the previous actions were completed.
- We reinforce that framing wins with a “so what?” (impact or next step) and writing notes live during the meeting both strengthen trust and make the manager feel valued.

Partner, Not A Boss

In this lesson, we talk about shifting the mindset from seeing an engineering manager as an authority figure to treating them as a partner whose incentives align closely with ours, unlocking far more growth and support.

- We emphasize that while EMs have real power (performance reviews, promotions, PIPs), relating to them through fear undermines trust, collaboration, and growth.
- We highlight that both we and our EM share the same fundamental goal: maximizing our impact, because our increased impact directly elevates their team impact.
- We explain that when we grow, deliver more, and navigate the org effectively, our manager looks better—so supporting our development is in their best interest.
- We encourage adopting a peer-like, partnership mentality rather than a hierarchical one, which creates more openness, trust, and meaningful dialogue.
- We reinforce the idea that adding value to our manager—through communication, initiative, and impact—encourages them to reciprocate with resources, advocacy, and opportunities.

Be Upfront About Your Goals

In this lesson, we emphasize the importance of sharing our performance goals openly with our EM so they can effectively advocate for us during performance reviews, which are human-driven, subjective, and highly dependent on manager alignment.

- We highlight that performance ratings are not generated by an objective system (i.e. they’re decided by people), so managers need clear insight into what rating we’re aiming for to advocate properly.
- We stress that “no negative feedback” does *not* mean we’re on track for a good rating or promotion; assuming this is one of the most common and costly mistakes ICs make.
- We reinforce that 1:1s are the central place to share our target rating or promotion timeline, whether we’re aiming for “meets,” “exceeds,” or a promotion, so we can then build a plan with our EM.
- We note that even if our goal is simply to maintain solid performance (e.g., a 3/5), we should still say so explicitly to avoid slipping into a low rating unexpectedly.
- We share a story illustrating how unchecked assumptions about performance can lead to disappointment—and how early, honest conversations could provide clarity and opportunities to improve.

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/>

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/>

How To Debug Effectively with AI As My Partner?

In this lesson we talk about how to debug effectively with AI as a partner during a coding interview, and when to use it versus when to rely on your own judgment.

- **Use AI to generate more test cases, but step through code yourself** — AI can rapidly produce test cases that would take much longer to write manually, but since it predicts words rather than executing code, it can reproduce the same bugs it originally introduced, making it unreliable for tracing logic line by line.
- **Avoid leaning on AI for debugging, but use it strategically when stuck** — rather than asking AI to find or fix a bug directly, prompt it to brainstorm multiple possible causes, which keeps you in the driver's seat, surfaces hypotheses for you to validate with your own judgment, and reduces the risk of confidently wrong AI suggestions.
- **Always rerun all tests after a fix, not just the failing one** — thorough test coverage lets passing tests serve as reliable verification; if your test suite is thin, compensate with more manual code inspection to avoid introducing regressions you won't have time to track down later.

Grow Fundamental Skills

We explain why growing fundamental, transferable skills is one of the most powerful ways to reduce layoff risk and strengthen long-term career resilience. Here are the core points from the lesson:

- There is far more to software engineering than coding, especially at scale, where communication, project management, leadership, and relationship-building determine whether work actually ships.
- Over-indexing on a specific technology, language, or product is risky, because teams, systems, and entire products can disappear, while transferable skills allow you to move across teams or companies.
- Engineers who can clearly design systems, think through edge cases, write thoughtful plans, and communicate tradeoffs create outsized impact compared to those who jump straight into code.
- So-called “soft skills” are actually the highest-leverage skills, because they compound across roles, companies, and industries, while technical tools and frameworks inevitably change.
- Focusing your time on high-leverage problems and influence, rather than low-level tooling details, is what drives career growth, adaptability, and durability during periods of layoffs.

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/>

Before The Meeting

In this lesson, we dig into how to prepare for 1:1s so they’re consistently high-value and how thoughtful prep not only improves the conversation but also prevents cancellations and protects our time.

- We advise seeding the 1:1 agenda at least 24 hours in advance so our EM can “pre-compute”—arriving prepared instead of reacting on the spot to complex issues.
- We recommend prioritizing agenda items, putting critical topics first in case time runs short, and leaving lower-priority items for follow-up on Slack if needed.
- We highlight that surprising a manager with heavy topics during the meeting leads to wasted time and weaker guidance, whereas advance notice empowers them to think deeply and respond effectively.
- We explain that sending a thoughtful agenda also makes it harder for the manager to cancel or move the meeting, since it clearly shows we’ve invested effort and have meaningful topics ready.
- We reinforce that consistent preparation creates smoother, more productive discussions and helps us extract maximum value from the limited 30 minutes we have each week.

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).

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.

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).

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.

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)

Promotion Expectation Examples (Good vs. Bad)

In this lesson, we show how vague career frameworks aren’t enough for real growth—and how transforming generic expectations into detailed, personalized criteria creates clarity, direction, and stronger promotion outcomes.

- We explain that most companies’ career matrices are extremely generic (“have broad influence in code review”), making them nearly useless for understanding what to *actually* do to reach the next level.
- We demonstrate how to replace vague statements with concrete, customized expectations that clearly outline the **delta** between current level and next level.
- We give an example from Meta: mid-level engineers (E4) typically review code only in their domain, while senior engineers (E5) are expected to review code across unfamiliar modules, broader systems, and even sister teams—showing multiplicative impact.
- We highlight that this level of specificity makes it far easier for us to understand, track, and execute on what “the next level” actually requires.
- We emphasize that crafting a detailed plan also has a perception benefit: it signals seriousness, ownership, and thoughtfulness—making managers more likely to advocate for our promotion.

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).

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/>

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/>

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/)

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.