Maximize Your Equity And Benefits In Tech [Part 14] - Stock Option Special Tax Considerations

When working at an early-stage startup, certain tax strategies can unlock major financial advantages—if you plan ahead. Here are the core points from the lesson:

- Qualified Small Business Stock (QSBS) can allow up to $10 million or 10x your basis in tax-free capital gains if you meet specific conditions, like holding actual shares for five years and your company having under $50M in assets at grant
- Early exercise and filing an 83(b) election can start the QSBS clock early and lock in low tax values, but they come with financial commitment and risk if you leave before fully vesting
- The 83(b) election must be filed within 30 days of exercising options and doesn't accelerate the two-year holding clock required for qualified ISOs
- Only $100K worth of ISOs can vest annually for favorable tax treatment—going above that limit risks converting some to NSOs with different tax implications

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/)

Become An Engineer Influencer [Part 16] - Editing

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.

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/>

Amazon Behavioral Interview [Part 20] - Taking An Unpopular Stance (Evaluation)

This video segment provides feedback on an individual's response to a behavioral interview question about a time when they took an unpopular stance.

- **Identified Shortcomings:** The feedback highlights that the interviewee's response, while demonstrating a willingness to disagree with leadership, may not be perceived favorably in an Amazon interview setting.
- **Amazon's "Backbone" Culture:** The feedback emphasizes the importance of the "backbone" principle at Amazon, which values individuals who not only express their opinions but also actively contribute to the success of the chosen course of action, even if it differs from their initial recommendation.
- **Alternative Approach:** The feedback suggests that a more effective response would have focused on how the interviewee, despite the initial disagreement, actively contributed to the success of the chosen strategy. This could include:
  - Emphasizing their commitment to implementing the decision effectively.
  - Describing how they provided valuable input and insights to improve the chosen approach.
  - Highlighting their contributions to mitigating potential challenges and ensuring the successful execution of the strategy.
- **Importance of Demonstrating Commitment:** The feedback emphasizes that Amazon values individuals who are not only willing to express their opinions but also demonstrate a strong commitment to the success of the team and the organization, regardless of whether their initial recommendations are adopted.

This analysis provides valuable insights into the expectations and values of Amazon's interview process, particularly regarding the "backbone" principle and the importance of demonstrating commitment and a collaborative approach.

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

Become An Engineer Influencer [Part 25] - Digital Products

Become An Engineer Influencer [Part 22] - Monetization Strategies

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).

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/>

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.

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/>

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/>

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.

Be Vulnerable

In this lesson, we highlight the importance of vulnerability in building a strong, supportive relationship with an engineering manager—because they can only help us if they understand what we’re going through.

- We stress that EMs can’t read minds; if we’re struggling, whether with a teammate, a project, or something personal, we should tell them so they can protect our well-being at work.
- We explain that good managers act like part-time therapists, and sharing when things are rough allows them to adjust workload, mediate conflicts, or at least offer support.
- We note that vulnerability builds trust, just like in friendships outside of work, and that trust significantly improves how effectively we collaborate.
- We clarify that we shouldn’t overshare, but we also shouldn’t hide everything—sharing selectively helps EMs understand who we are and how to help.
- We reinforce that showing vulnerability increases the “trust meter,” strengthens the partnership, and ultimately unlocks more powerful support from our EM.

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.

How Do I Talk Without Sounding Chaotic?

In this lesson we talk about how to communicate effectively during an AI-assisted coding interview without sounding scattered, frantic, or going completely silent.

- **Avoid the two extremes** — narrating every single keystroke comes across as junior, while going silent for several minutes leaves the interviewer in the dark; the goal is to sound controlled and intentional somewhere in between.
- **Choose the communication model that fits your thinking style** — Model A is light, continuous commentary as you work ("I'm pulling the date out of each event to populate the per-day set"), while Model B is stating your high-level intent upfront, working silently for 60–90 seconds, then summarizing what you did before repeating the cycle.
- **Practice talking while you work before the interview** — like touch typing, verbalizing your thought process while solving a problem is a skill that needs to be rehearsed, not figured out on the spot; a few mock sessions with a friend or mentor can make a significant difference.

Finding Scope To Grow

We walk through how to evaluate whether a team offers real *scope for growth*—one of the most important factors for long-term career progression.

Here are the core points from the lesson:

- We suggest assessing a team’s track record of major launches: frequent, meaningful wins signal room for impactful work, while long dry spells often indicate a team that is either too new (unstable) or too mature (no hard problems left to solve).
- We point out that very mature orgs like core Facebook Feed may be so optimized that it’s nearly impossible to produce meaningful results, making promotions extremely difficult; meanwhile, very new orgs offer big scope but come with chaos and uncertainty.
- We recommend examining the level distribution on the team: a healthy mix of engineers more senior *and* more junior than you enables both mentorship (learning) and leadership (scope creation), whereas being the most junior or most senior can severely limit growth.
- We advise deeply evaluating the engineering manager’s track record—whether they have successfully grown people from junior → mid-level → senior → staff—because a strong EM accelerates your development, while a newly minted or unproven EM introduces career risk.

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.