Role-Specific System Design Focus

In this part of the system design section, we explore how **ML system design interviews vary depending on the role and level** we’re applying for. Knowing what’s emphasized helps us tailor our preparation strategically.

- For **product-focused ML roles**, we focus on modeling choices, feature engineering, and connecting ML decisions to tangible business impact.
- **ML infrastructure roles** emphasize architecture, scalability, reliability, and serving systems—including distributed training and feature stores.
- **Research-oriented roles** dive into experimentation frameworks, novel modeling techniques, and how to transition prototypes into production.
- In **ML generalist roles** (common at startups or smaller teams), we’re expected to demonstrate a balanced understanding across the full ML lifecycle.
- Importantly, **interview expectations are tied more to seniority level than company size**—a senior role at a startup may involve deeper system questions than a junior role at a large tech company.

Next, we’ll learn a system design framework that can flex across these scenarios and help us organize our thinking effectively.

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/>

Notebook Interview Example: Ad Click Prediction

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

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

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

If you want to learn even more from Yayun:

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

Model Training

In this segment, we cover **model training**, a phase where many candidates fall into autopilot. To stand out, we need to show thoughtfulness, adaptability, and a clear connection between our choices and the problem at hand.

- **Mediocre performance** involves picking a default model (e.g., logistic regression or random forest), using out-of-the-box settings with no tuning or justification.
- **Strong candidates** choose models deliberately, explaining how their selection fits the **data characteristics, business goals, or system constraints**.
- We improve performance through **hyperparameter tuning**, using techniques like grid search or informed manual adjustments based on EDA insights.
- When appropriate, we apply **ensemble methods** (e.g., boosting, bagging, stacking) to push performance further.
- Most importantly, we demonstrate that our modeling choices are **intentional and grounded in context**, not just rote or random experimentation.

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/>

Feature Engineering

In this part of the course, we focus on **feature engineering**, a critical area where interviewers differentiate between average and exceptional ML candidates. This is our opportunity to show not just technical skill, but creativity and judgment.

- **Mediocre feature engineering** relies on basic, mechanical steps—like one-hot encoding categoricals or normalizing numericals—without much customization.
- **Stellar candidates go further**, creating new features from raw data (e.g., extracting time-of-day from timestamps, or combining variables into meaningful interactions).
- We show thoughtfulness by **dropping irrelevant features**, checking for **multicollinearity**, and aligning features closely with the problem's objectives.
- We demonstrate versatility by handling **complex data types**—such as transforming freeform text, working with embeddings, or aggregating time series effectively.
- Ultimately, we recognize that **feature engineering can matter more than the model itself**, and our job is to shape the data so that the model can truly learn from it.

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/>

Round Structure

In this section, we dive into one of the most important—and often most hands-on—parts of the ML interview process: the practical machine learning coding and modeling round. This is where we demonstrate how we think and work like real ML practitioners.

- We’ll usually work in a **Jupyter notebook environment**, either in a take-home format or during a live 60–90 minute session.
- The task typically involves a **real-world problem** like text classification or demand forecasting, where we're expected to deliver an end-to-end ML workflow.
- Unlike algorithm rounds, we’re **encouraged to use standard ML libraries** (e.g., pandas, scikit-learn, TensorFlow, PyTorch) to solve the problem efficiently.
- We’ll be evaluated on how we **explore data, engineer features, choose models, tune hyperparameters**, and **communicate our results**.
- This round is about **thinking like an ML practitioner**, showing that we can handle messy, ambiguous problems using practical tools to deliver meaningful solutions.

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/>

The CLEAR Framework

In this part of the course, we learn how to speak about machine learning concepts in a way that’s clear, confident, and impactful. The instructor introduces a practical, repeatable framework—CLEAR—that helps us explain any concept with structure and depth.

- **C is for Conceptualize**: Start with a high-level, simple definition of the concept (e.g., logistic regression predicts binary outcomes).
- **L is for Link**: Connect the concept to related ideas or typical use cases, showing broader understanding.
- **E is for Example**: Provide a real-world application to ground the concept in practice.
- **A is for Advantages and Disadvantages**: Discuss trade-offs to demonstrate critical thinking.
- **R is for Result**: Explain how success is measured using appropriate evaluation metrics.

By using the CLEAR framework, we present ourselves as organized, knowledgeable, and experienced—even when discussing foundational topics.

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 23] - Conclusion

Congratulations on making it to the end of this course! Here are core points from the lesson:

- Know the details of your equity, 401k, and ESPP plans, and set calendar reminders for things like stock option expirations, enrollment windows, and rollover opportunities
- Automate what you can only after understanding your holdings—review RSU tax withholdings, grant docs, and whether you’re using pre-tax vs. Roth intentionally
- Explore advanced options like mega backdoor Roths if your 401k allows them, and ensure you're not missing out on your employer match or other benefits
- Review your health plan and associated perks like HSAs, FSAs, and wellness stipends, and use reminders to avoid letting free money go to waste

If you want to get additional support from Andy:

- [Connect with Andy on LinkedIn](https://www.linkedin.com/in/andymoranio)
- [Receive other bonuses, leave feedback, and/or sign up for Andy’s newsletter](https://subscribepage.io/taroequitycourse)
- [Schedule a consultation](http://adastrafp.com/contact-us/)

Build Deep Relationships Quickly In Tech [Part 10] - "Good To Know" People

You never know who's going to come in and completely change your career for the better. This lesson teaches you:

- What "good to know" connections are
- Why networking with these connections can actually be quite easy
- The most powerful way to identify "good to know" connections

Build Deep Relationships Quickly In Tech [Part 9] - Tactical Stakeholders

To succeed at work, you need to have the right people trust you. This lesson teaches you:

- What tactical stakeholders are and why they're crucial for your network
- How tactical stakeholder relationships become more and more important as you become more senior
- Who your tactical stakeholders are and how they change as you level up as an engineer

SWE Internship Survival Guide [Part 13] - Code Review

As an intern, your ability to navigate code reviews can significantly impact your chances of getting a return offer. It’s not just about code correctness — how you engage with feedback and your team matters just as much. Here are the core points from the lesson:

- Code reviews are mandatory for shipping code and are a key mechanism for maintaining quality, sharing knowledge, and passing on team culture
- Interns often struggle with code review because it’s rarely taught; expect lots of feedback early on, and aim to show progress by reducing repeated comments over time
- Your *dynamic* — how open, curious, and quick you are in responding to feedback — is often more important than just the *substance* of your code
- Always respond quickly to feedback, ideally within an hour, and never land a PR with unresolved comments unless clearly communicated and agreed upon
- For large or confusing feedback, escalate to a synchronous conversation (e.g., a call) instead of dragging it out over multiple asynchronous review rounds

SWE Internship Survival Guide [Part 12] - Code Quality

Writing a lot of code isn’t enough to secure a return offer — it also needs to meet your team’s quality standards. Clean, thoughtful code makes it easier for others to review, trust, and approve your changes. Here are the core points from the lesson:

- Poor quality code, even if it’s high in volume, will cost you a return offer
- High-quality code means making your changes easy to review by including a clear commit message, test plan, and relevant context
- Interns often overlook non-code details like documenting edge cases and adding screenshots or videos to demonstrate what changed
- Good code quality is about reducing friction for your reviewers — make their job easier and they’ll be more likely to trust and support you

Take the code quality course here to instantly boost your return offer chances by +50%: [Level Up Your Code Quality As A Software Engineer](https://www.jointaro.com/course/level-up-your-code-quality-as-a-software-engineer/)

SWE Internship Survival Guide [Part 9] - Build In Public

It’s not enough to quietly ship code during your internship—sharing your progress publicly is a key strategy for visibility and success. Here are the core points from the lesson:

- By default, no one except your mentor may know what you’re working on, so it’s your job to build in public and generate awareness
- Visibility leads to stronger relationships, more feedback, and more people who can advocate for you when return offer decisions are made
- Intern projects often aren’t on the critical path, making it even more important to market your work through demos, updates, and conversations
- Reusing assets like screenshots or videos from your pull requests is a great way to showcase progress
- Use team standups, Slack channels, emails, and meetings to share what you’ve built and invite feedback, always including links and tagging collaborators to maximize engagement

SWE Internship Survival Guide [Part 6] - Onboard Efficiently

Onboarding efficiently is one of the most critical phases of your internship. Here are the core points from this lesson:

- Embrace being new—ask questions early and often, seek feedback, and don’t waste time being stuck on problems for longer than an hour
- Prioritize setting up your environment and making small code changes early to learn workflows and gain confidence
- Build relationships immediately: meet with your intern manager and schedule one-on-ones with at least two other teammates for broader support and perspective
- The sooner you engage and request help, the more value you’ll get from the relationship—and the less it will feel awkward or overdue later in the internship

Take the full onboarding course here: [The Complete Onboarding Guide For Software Engineers](https://www.jointaro.com/course/the-complete-onboarding-guide-for-software-engineers-succeeding-when-youre-new/)

Ship & Grow 500k+ User Side Projects [Part 16] - Word Of Mouth

The most organic source of traffic is happy users simply telling their network how awesome your product is. This lesson teaches you:

- The main metric to understand when it comes to your word of mouth
- 2 product mechanisms you can add to juice up your word of mouth
- How to make an effective sharing flow, explained with an in-depth example
- How to make a compelling referral flow, shown with famous examples

Airbnb blog post about how they sharply refined their sharing flow (an oldie but a goodie): <https://medium.com/airbnb-engineering/growth-at-scale-getting-to-product-sharing-fit-ccb4b501ecf>

Ship & Grow 500k+ User Side Projects [Part 7] - KISS

Engineers naturally gravitate towards complexity, and it shows in how they communicate and design things. This is a problem when it comes to side projects. This lesson teaches you:

- The beautiful, often under-appreciated value of simplicity
- Assumptions you shouldn't make about the end-user
- What it means to KISS (Keep It Simple, Stupid), explained with an in-depth example

Part 8 [RBP] Next Steps With React

Follow Florian on various social media platforms like [YouTube](https://www.youtube.com/c/CodinginFlow), [Instagram](https://www.instagram.com/codinginflow/), [TikTok](https://www.tiktok.com/@codinginflow), and [X/Twitter](https://x.com/codinginflow) for more web development content and updates. Mailing list: [CodingInFlow.com/newsletter](http://CodingInFlow.com/newsletter)

Links:

- Florian’s free web development playlist on YouTube: <https://www.youtube.com/playlist?list=PLrnPJCHvNZuDG6BWjUFVxeWyYlve_SVJO>
- Code link: <https://github.com/codinginflow/react-best-practices/branches/stale>

Part 7 [RBP] Use Fragments Instead Of Divs

This lesson focuses on using React Fragments to improve the structure and styling of your components when rendering multiple elements.

- **The Problem with Divs:** Learn how wrapping elements in unnecessary divs for rendering can lead to cluttered DOM structures and styling issues.
- **Introducing Fragments:** Discover how to use Fragments (empty tags `<> </>`) to group elements without adding extra nodes to the DOM, resulting in cleaner and more efficient code.
- **Benefits of Fragments:** Understand how Fragments help maintain proper styling, improve code readability, and optimize your application's performance by reducing unnecessary DOM elements.

Links:

- Florian’s free web development playlist on YouTube: <https://www.youtube.com/playlist?list=PLrnPJCHvNZuDG6BWjUFVxeWyYlve_SVJO>
- Code link: <https://github.com/codinginflow/react-best-practices/blob/fragments/src/app/page.tsx>

Part 4 [RBP] Clean Up Your Fetch Requests

This lesson focuses on preventing race conditions that can occur when making multiple fetch calls in React components, leading to inconsistent UI behavior and a poor user experience.

- **Identifying Race Conditions:** Understand how race conditions happen when asynchronous fetch requests resolve in an unexpected order, particularly with slow or unreliable network connections.
- **Strategies for Avoiding Race Conditions:** Learn how to use tools like SWR or React Query to manage fetch requests and automatically handle race conditions, or implement manual cleanup logic with an "ignore" flag to discard outdated responses.
- **Improving Code Structure:** Discover how to keep your fetch-related code organized and maintainable, especially when dealing with loading and error states, setting the stage for cleaner solutions covered in the next lesson.

Links:

- Florian’s free web development playlist on YouTube: <https://www.youtube.com/playlist?list=PLrnPJCHvNZuDG6BWjUFVxeWyYlve_SVJO>
- Code link: <https://github.com/codinginflow/react-best-practices/blob/fetch-cleanup/src/app/page.tsx>

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

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

- **Strengths:**

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

- **Areas for Potential Improvement:**

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

- **Overall:**

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

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

Amazon Behavioral Interview [Part 30] - Building Trust With Stakeholders (Evaluation)

This video segment discusses the quality of an interview conducted by a person named Alan.

- **The Interviewer's Approach:**

  - **Unclear Questioning:** Alan initially asked a general question about building trust, which is not a typical behavioral interview question.
  - **Unnecessary Detour:** This initial question led to a lengthy discussion that was not directly relevant to assessing the interviewee's skills or experiences.
  - **Awkward Transition:** Alan then abruptly shifted to a more relevant question about a time the interviewee failed to meet a commitment, but the transition felt awkward and disjointed.

- **Concerns about Interview Quality:**

  - **Lack of Clear Focus:** The interviewer's approach seemed unstructured and lacked a clear focus on assessing the interviewee's skills and experiences.
  - **Potential for Bias:** The interviewer's personal style and communication approach could potentially bias the interview outcome, particularly for interviewees who are less experienced or less comfortable with unexpected questioning.
  - **Limited Quality Control:** The video highlights a concern about the lack of quality control for interviewer performance and the potential impact on interview outcomes.

- **Recommendations:**

  - **Focus on Clear and Relevant Questions:** Interviewers should focus on asking clear, concise, and relevant behavioral questions that directly assess the candidate's skills and experiences.
  - **Maintain a Structured Approach:** A structured interview approach helps ensure that all candidates are evaluated fairly and consistently.
  - **Improve Interviewer Training:** Investing in interviewer training programs can help to improve the quality of interviews and ensure that all interviewers are equipped with the necessary skills and knowledge to conduct effective and unbiased interviews.

This analysis highlights the importance of conducting high-quality interviews to ensure fair and accurate assessments of candidate qualifications.

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

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.