How To Apply The 80/20 Rule To Leetcode Prep

Even Alvin, who has mastery over Data Structures and Algorithms, would not pass all the tech interviews!

Course: <https://www.jointaro.com/course/crash-course-data-structures-and-algorithms-concepts/>

Preparation Strategy

In this final part of the ML algorithm coding interview section, we shift focus to **preparation strategy**. The goal is to build muscle memory, deepen our understanding, and simulate real interview pressure before the actual interview day.

- We should **implement key algorithms ahead of time** and store them in a personal repo—don’t wait until the interview to figure them out.
- Using **toy datasets** helps us quickly verify correctness and develop intuition for how outputs should behave.
- We need to **time ourselves**, practicing full implementations within a 45-minute window to build speed and realism under pressure.
- Studying the **underlying math**—like gradient descent, entropy, and loss functions—enables us to debug faster and explain our logic clearly.
- Finally, we should **review common optimizations**, so even if we don’t implement them, we can speak to how we’d improve runtime or memory use.

By treating preparation like athletic training—intentional, timed, and iterative—we give ourselves the best shot at performing with confidence and depth in the real interview.

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/>

General Career Direction Principles

We close with guidance for people who feel completely unsure about where to go next in their tech careers.

Here are the core points from the lesson:

- We suggest that early-career engineers, when possible, start with a big tech company for 1–2 years to gain stability, strong compensation, and a solid learning foundation—then use that platform to explore what they truly want.
- We recommend deliberately experiencing different company archetypes (startup, midsize, big tech) because each environment teaches different skills, exposes us to different cultures, and clarifies our long-term preferences.
- We emphasize that career needs change with age and life complexity—stability tends to matter more in our 30s and 40s—and that most big-tech engineers ultimately transition to smaller companies to find scope and growth.
- We add that when in doubt, choose a company where software engineering is core to the business, not a peripheral function; being in the “engine room” of a product-focused tech company dramatically increases learning, opportunity, and long-term career resilience.

Paper Reading: MyRocks: LSM-Tree Database Storage Engine Serving Facebook's Social Graph

Session led by [Dharin Shah](https://www.linkedin.com/in/dharinshah/)

**Event description**

Facebook uses MySQL to manage tens of petabytes of data in its main database named the User Database (UDB). UDB serves social activities such as likes, comments, and shares. In the past, Facebook used InnoDB, a B+Tree based storage engine as the backend. The challenge was to find an index structure using less space and write amplification.

LSM-tree has the potential to greatly improve these two bottlenecks. RocksDB, an LSM tree-based key/value store was already widely used in variety of applications but had a very low-level key-value interface. To overcome these limitations, MyRocks, a new MySQL storage engine, was built on top of RocksDB by adding relational capabilities. With MyRocks, using the RocksDB API, significant efficiency gains were achieved while still benefiting from all the MySQL features and tools. The transition was mostly transparent to client applications.

The paper was published at the **International Conference on Very Large Databases (VLDB)**: [**https://research.facebook.com/publications/myrocks-lsm-tree-database-storage-engine-serving-facebooks-social-graph/**](https://research.facebook.com/publications/myrocks-lsm-tree-database-storage-engine-serving-facebooks-social-graph/).

Paper authors:

- [**Yoshinori Matsunobu**](https://www.linkedin.com/in/yoshinorimatsunobu/)
- [**Siying Dong**](https://www.linkedin.com/in/siyingdong/)
- Herman Lee

Common Questions

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>

In this video, we explore the behavioral side of engineering manager (EM) interviews, which focus heavily on leadership abilities. Each question is designed to assess core leadership competencies, and mastering these areas is critical for success.

- **Hiring & Growing Talent**: Interviewers look for evidence of managing underperformers, nurturing strong performers, and navigating hiring and talent review processes.
- **Delivery Under Pressure**: Candidates must demonstrate the ability to lead high-quality project deliveries on tight timelines, despite resource constraints or setbacks.
- **Stakeholder Management & Influence**: Expect to showcase how difficult feedback was delivered or how stakeholders were influenced for team benefit, often through challenging conversations.
- **Conflict Resolution & Judgment**: Questions will probe how candidates handle internal or external conflicts, as well as how strategic decisions were made with limited data.
- **Vision & Execution**: Interviewers want to hear about setting long-term vision, taking calculated risks, digging deep into complex problems, and operating effectively across all levels.

Coding Round Grades

In this segment, we break down what interviewers really look for in a machine learning coding round. It’s not just about solving the problem—it's about how we think, communicate, and write code that reflects real engineering practices.

- We understand that **weak performance** stems from messy code, poor use of data structures, or inability to explain complexity.
- **Mediocre performance** may reach the right answer but relies heavily on interviewer help, signaling a lack of confidence or strategy.
- A **great performance** involves writing clean, efficient code and clearly explaining trade-offs and scalability.
- To be **exceptional**, we should offer multiple solutions, consider edge cases, and write production-quality code.
- We improve by following a **systematic approach**, thinking out loud, handling edge cases, and prioritizing clean, readable code—just like real ML engineers do in production.

If you want to learn even more from Yayun:

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

Paper Reading: Beyond Next-Token Prediction: Enhancing Language Models with Multi-Token Outputs

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

In this talk, we will look at groundbreaking research on advancing the training of large language models. Traditional models like GPT and Llama rely on next-token prediction, which can be expensive and time-consuming, but this research suggests a more efficient approach: multi-token prediction.

**Key Highlights:**

- **Enhanced Sample Efficiency**: Training models to predict multiple future tokens at once increases sample efficiency, leading to significant improvements in downstream capabilities.
- **Innovative Architecture**: By employing n independent output heads on a shared model trunk, better performance with no additional training time can be achieved.
- **Superior Performance**: 13B parameter models outperform strong baselines on generative benchmarks, solving 12% more problems on HumanEval and 17% more on MBPP.
- **Algorithmic Reasoning**: Multi-token prediction fosters the development of induction heads and enhances algorithmic reasoning capabilities.
- **Faster Inference**: Models trained with 4-token prediction are up to 3 times faster during inference, even with large batch sizes.

Join us to explore how multi-token prediction can revolutionize language model training and performance, especially for coding and natural language tasks.

Don't miss this opportunity to learn about the future of language model training and its practical applications!\
\
Paper: [**https://arxiv.org/pdf/2404.19737**](https://arxiv.org/pdf/2404.19737)

Taro Book Club: The Pragmatic Programmer - Session 1

If you don't have a Taro Premium membership, you can get it with a discount with Bikram's special link here: [**https://www.jointaro.com/r/bikramk104/**](https://www.jointaro.com/r/bikramk104/)

In this #taro-bookclub discussion, we'll go over Chapter 1 of [**"The Pragmatic Programmer"**](https://www.amazon.com/Pragmatic-Programmer-Journeyman-Master/dp/020161622X) by Andrew Hund & David Thomas

Here's the agenda:

- It's Your Life
- The Cat Ate My Source Code
- Software Entropy
- Stone Soup and Boiled Frogs
- Good Enough Software
- Your Knowledge Portfolio
- communicate

Please send your questions ahead of time to [**Bikram Kashyap**](https://www.linkedin.com/in/kashyapbikram/) in the [**Taro Premium**](https://www.jointaro.com/membership/) Slack, so that they can be included in the discussion. *If you don't have the book or want more details on how this all works, check out the #taro-bookclub channel in the Taro Premium Slack as well!*

Get ready for another engaging and informative session!

Paper Reading: Self-supervised Vision Transformers (Foundational Model)

Session led by [Mandar Deshpande](https://www.linkedin.com/in/mandardeshpande1995/)

Dino is a self-supervised learning approach for computer vision tasks. It leverages vision transformers without using any labeled data. Dino v2 introduces several improvements over its predecessor, enhancing performance and scalability. It achieves state-of-the-art results on various benchmarks. Notably, Dino v2 demonstrates strong performance on downstream tasks such as image classification and object detection.

We will be walking through

- Basics of computer vision
- Self supervised learning
- ViT (vision transformers)
- DINO
- DINO v2 model

Paper link - [**https://arxiv.org/abs/2304.07193**](https://arxiv.org/abs/2304.07193)

[Event link](https://www.jointaro.com/event/paper-reading-dino-v2-ie-foundational-model-for-computer-vision/)

Going From Good To Unforgettable

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>

To turn our engineering manager interviews from good to unforgettable, we need to move beyond structured responses and tap into a deeper level of storytelling that integrates our leadership philosophy, self-awareness, and clarity of thought. This video teaches us how to do exactly that by weaving together frameworks, personal reflection, and strategic communication.

- **Pair STAR with Philosophy and Frameworks:** We start with the STAR method (Situation, Task, Action, Result), but go deeper by clearly highlighting the conflict in the situation and pairing our actions with leadership philosophies and mental frameworks. This helps reveal not just what we did, but *why* we did it and how we think as leaders.
- **Answer the “Question Behind the Question” (QBQ):** Every interview question has a hidden layer—what the interviewer *really* wants to know. For example, “Have you dealt with underperformance?” isn't just about that event; it’s probing our ability to have hard conversations, uphold standards, and lead consistently.
- **Embed Systems, Frameworks, and Learnings:** Our answers should demonstrate how we operate systematically. For instance, when describing how we mediate conflict, we explain our decision-making framework and show how it was applied in real situations, reinforcing our credibility and maturity as leaders.
- **Think Beyond the Surface:** Questions like “How do you motivate your team?” are really asking about our leadership style, understanding of technical people, and ability to balance individual, team, and org-wide goals. We must address these nuances and explain how motivation is an ongoing, intentional process.
- **Use the EM Interview Formula:** The real formula for standout interviews is *(QBQ) + (Philosophy + Systems + Frameworks) + (Real Story + Learnings).* This holistic approach helps us craft answers that are authentic, insightful, and tailored to what EM interviewers truly care about.
- By leaning into this deeper method, we don’t just pass interviews—we leave a lasting impression.

[Paper Reading] Deploy ML Models from Lab to Production

Session led by: [Akshika Wijesundara PhD](https://www.linkedin.com/in/akshika/)

If you don't have a Taro Premium membership, you can get it with a discount with Akshika's special link here: [**https://www.jointaro.com/r/akshikw802/**](https://www.jointaro.com/r/akshikw802/)

**WHAT YOU'LL LEARN**

- **Discover the benefits of CI/CD pipelines:** Learn how CI/CD pipelines streamline the model building, testing, and deployment processes, enabling rapid iteration and continuous improvement.
- **Containerize your ML code:** Understand the steps and advantages of containerizing your machine learning code to ensure consistency, portability, and efficient resource management.
- **Leverage the AWS stack for ML workloads:** Explore how to effectively utilize AWS as your cloud infrastructure provider to run and scale your machine learning workloads with ease and reliability.

**WHY THIS TOPIC MATTERS**

MLOps isn't just about fancy models - it's about getting them working for real! Learn how to deploy, maintain, and collaborate on ML projects, making you a highly-demand expert who can turn machine learning into real-world impact.

Ineffective Evaluation Techniques

We close this section by calling out two common but misleading ways people try to evaluate companies and why we should avoid them.

Here are the core points from the lesson:

- We urge you not to judge a company based on how much you liked or disliked your recruiter or interviewers, because you’ll almost never work with them, and your network’s lived experience inside the company is a far better signal of culture and fit.
- We caution against spending lots of time analyzing financial statements and balance sheets unless you’re actually trained in that area; as engineers, we’re likely to misinterpret the numbers and waste time.
- We recommend instead focusing on conversations about the company’s vision, growth, and people, since fast-growing companies can change dramatically in just a few years and those qualitative signals tend to be much more predictive for your career.

Leverage Your Unfair Advantage

We explain how to accelerate your job search after a layoff by intentionally leveraging the advantages already on your résumé. Here are the core points from the lesson:

- We should leverage company brand as an unfair advantage, since recognizable names (Big Tech, well-known startups, or respected niche companies) carry trust and credibility that immediately raise our odds versus the average applicant.
- We can increase our chances by targeting adjacent companies, customers, or competitors where our background naturally fits, instead of applying randomly to roles with no contextual advantage.
- We should confidently lean into domain and product association—using the halo effect of teams, products, or problem spaces we worked on—because perceived relevance often matters more than perfect technical alignment.

Getting Extra Help

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>

Get 1 on 1 coaching from Alex here (mention Taro and get 10% off): <https://www.alexkliotzkin.com/>

To succeed in Engineering Manager interviews, we need to do more than prepare our answers—we must *practice intentionally and iteratively*. This video highlights how structured practice can dramatically improve our communication, clarity, and confidence.

- **Mentally Rehearse with Visualization:** We should regularly visualize being in the interview, anticipating questions, and mentally walking through our stories and responses. This builds fluency and composure.
- **Write Stories for Guidance, Not Scripts:** Documenting our stories helps solidify what we want to say, but we shouldn’t memorize or regurgitate them. The goal is clarity on the *core signals* and themes we want to convey.
- **Record and Review Ourselves:** Watching ourselves on video is uncomfortable but invaluable. It reveals nervous tics, verbosity, or lack of clarity, and helps us iterate quickly.
- **Practice with Others for Realistic Feedback:** Mock interviews with someone—ideally a professional coach—simulate real pressure and provide tailored feedback. This is the most effective way to get interview-ready

Level-Specific Expectations

As with system design interviews, behavioral interview expectations increase with seniority. This round is our chance to bring our resume to life by showing how we think and lead as ML engineers.

- **Junior ML engineers** should focus on clearly explaining their contributions and demonstrating how they applied ML techniques with guidance.
- **Senior engineers** need to showcase how they led ML projects end-to-end, made thoughtful trade-offs, and influenced product or technical decisions.
- **Staff-level engineers** should highlight strategic, cross-team contributions—like building reusable ML platforms, solving ambiguous problems, and mentoring others.

This is where we demonstrate not just *what* we’ve done, but *how* we think—our process, leadership, and impact.

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/>

This Facebook Engineering Director Quit To Sail The World

After more than 10 years working at Meta (Facebook), James quit to work on his boat full-time. While sailing, he also contributes to open-source! <https://github.com/tinyplex/tinybase>

STAR

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>

Engineering manager interviews focus heavily on **behavioral assessments**, and using a structured response framework like **STAR** can help clearly demonstrate leadership competencies and impact. STAR stands for **Situation, Task, Action, and Result** and is widely recommended for leadership roles due to its clarity and depth.

- **Situation**: Set the context of your story—when, where, and why it happened—so the interviewer understands the environment and background.
- **Task**: Define your responsibility in that situation and any challenges you faced while addressing it.
- **Action**: Detail the specific steps *you personally* took, what you owned, and how you worked with others.
- **Result**: End with measurable outcomes like cost savings, quality improvements, time to market, or customer/team impact. Show the success and its scale.

Master the Art of Receiving Quality Feedback (from an ex-Meta Staff Researcher)

In this time of uncertainty in the workplace and job market, what’s one skill you can develop that would **yield disproportionate rewards**?

It’s hard to pinpoint *the one* skill, but I believe the **ability to ask for, receive, and iterate on feedback** is incredibly important. Adaptability is what helps us not just survive, but thrive—and adaptability begins with how well we take in and work with feedback.

When you learn to process feedback effectively, **you develop a kind of likability and professional superpower** that organizations and leaders find hard to ignore.

We hear feedback all the time, so what exactly are we trying to get better at? Most of the feedback we receive is vague and hard to act on. Think about examples like “improve your communication” or “increase your coding speed”—where do you even start? The **key is in asking for feedback** proactively, clarifying and chunking it down into something actionable, working on it, and then circling back to check if you’ve understood and implemented it well.

In this talk, we’ll learn how to do exactly that—a process I call “**exhausting the feedback process**,” a phrase I often use in my coaching practice. We’ll also explore how to handle feedback that’s just plain off the mark (spoiler: it’s rarely useful to say that outright to the person giving it).

Yogi on LinkedIn: <https://www.linkedin.com/in/yogi1/>

Leverage Your Allies In The Process

This segment highlights a commonly overlooked advantage in the interview process: *building relationships with your recruiter and hiring manager*. They can be powerful advocates if approached thoughtfully.

- **Leverage your recruiter** by asking proactive questions about the interview structure, topics, and panel. They can offer inside tips that guide your preparation.

- **Treat the recruiter as a partner**, not just a scheduler. Showing curiosity and seriousness can reflect well on you.

- **Engage your hiring manager** with thoughtful questions about the team’s goals, challenges, and how your experience aligns with their work.

- **Recognize their influence**—hiring managers often hold sway in final decisions, even in committee-driven processes.

- **Use every conversation as a chance to stand out**, demonstrating preparation, alignment, and initiative.

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/>

Take Some Time To Reflect

We wrap up by reframing layoffs as a rare opportunity to pause, reflect, and intentionally design the next chapter of your career. Here are the core points from the lesson:

- A layoff often comes with severance and time, which is an incredibly valuable resource that gives you space to step off the hamster wheel and think clearly.
- Taking days or even weeks to reflect is worthwhile, since deep self-introspection about career direction, values, and long-term goals is difficult but high leverage.
- Being laid off can surface important questions you may have ignored while grinding, including whether you enjoy your role, your function, or even the tech industry at all.
- Boredom and distance from day-to-day work can spark creativity, learning, and exploration that are nearly impossible when you’re constantly shipping and firefighting.
- With persistence, reflection, and conversations with trusted people, a layoff can become a pivot point that leads to a more aligned, fulfilling next step—even if that path looks very different than before.

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.