Gilad’s Take: Missed Estimates

In this exercise, we explore how to manage an individual contributor who is consistently underperforming—missing estimates or failing in execution. This presents a valuable opportunity for us to create both individual and team-wide impact.

- We help the individual grow by **improving their estimation and execution skills**—through coaching, targeted training, mentorship, and root cause analysis.
- We aim to **understand the specific failure points** by collecting data and maintaining an open yet high-bar conversation with the individual.
- We don't just focus on the individual; we **expand our lens to the team**, recognizing that underperformance affects teammates who rely on timely outputs.
- To support the broader organization, we look for ways to **minimize the blast radius**, reducing the negative impact on the surrounding team while working toward resolution.
- This is about **maximizing delta**: helping one person improve while also protecting and enabling the rest of the group to thrive.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

Missed Estimates

In this segment, we’re challenged with a new scenario: an individual contributor consistently misses their own estimates. This is our opportunity to apply the essence of management—creating positive, sustainable change in organizational performance.

- We frame the issue not just as a problem to fix, but as an opportunity to **create a delta** in this person’s effectiveness and the team’s overall reliability.
- Our goal is to **understand the root cause**—is it due to optimism bias, poor time management, lack of clarity, or systemic issues in the planning process?
- We can introduce **structured changes**: better estimation techniques, mentoring, regular retrospectives, or adjusting sprint planning to account for variability.
- We aim for improvements that are **sustainable**—equipping the individual with tools or frameworks they can continue to use independently.
- As always, our success isn’t in reacting to the problem but in **proactively shaping a better outcome** that would not have happened without our involvement.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

Gilad’s Take: Motivated IC Auto-Didact

In this video, we deepen our understanding of the manager's role by applying it to a scenario with a high-performing, self-motivated team member. The key takeaway is that our value as managers lies not in the growth that would happen without us, but in the change we actively create.

- We remind ourselves that our **job is to create a delta**—a positive, sustainable improvement that wouldn’t have happened otherwise.
- We can influence growth in **two major ways**: by increasing its **efficiency** (helping the individual grow faster) and its **effectiveness** (guiding them toward more impactful areas of development).
- Efficiency may involve assigning growth-aligned projects, introducing mentors, or curating resources that accelerate learning.
- Effectiveness may require **redirecting energy** toward skills with greater business impact, such as shifting from technical specialization to leadership development.
- Ultimately, we commit to **seeking out and enabling the deltas**—changes we directly cause—because that's where our impact as managers truly lies.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

Motivated IC Auto-Didact

In this segment of the course, we begin translating theory into practice by applying the essence of management—creating positive and sustainable change—to real-life situations. We're guided through four exercises that challenge us to think creatively and proactively like effective managers.

- We approach each scenario by recalling that our role as managers is to **generate positive, lasting improvement** in our organization’s performance.
- We recognize that the scenarios are vague on purpose, so we must **draw from our own experiences** to interpret and respond thoughtfully.
- In the first exercise, we examine how to support a **highly motivated, self-learning individual contributor**, and consider what impact we can create to further enable their growth and contribution.
- We are encouraged to identify the **Delta**—the change we can influence or initiate—and write down our strategy for each case, reinforcing our understanding through reflection and application.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

What Do Managers Do?

Part Two of the *Essence of Management* course invites us to deeply reflect on the core question: **What do managers actually do?**

- We are encouraged to pause and **write a one-line summary** of the manager’s role—not our personal job, but the purpose of the managerial function itself.
- This section emphasizes **active engagement**, urging us to think, reflect, and put ideas into our own words rather than passively consuming content.
- The reflection exercise sets the foundation for the next module, where we’ll compare our answers to a more structured definition of management.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

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/>

Challenge Yourself, Be Authentic

This closing segment reinforces the importance of authenticity and adaptability during the interview process, especially in ML interviews where surprises are common.

- **Be ready for the unexpected**—interview questions may push you outside your comfort zone, but your response and thought process matter more than perfection.
- **Think out loud**—articulate your assumptions, logic, and uncertainties to help interviewers understand how you approach problems.
- **Show your passion**—talk about what genuinely excites you in ML, whether it's recommender systems, data scale, or experimentation.
- **Connect your interests to the company’s mission or team goals**—this helps show alignment and genuine motivation.
- **Remember, interviewers are hiring a person, not just a resume**—let your curiosity and drive come through.

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/>

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/>

Creating A Customized Study Plan

**In this segment, we’re guided through how to create an effective, targeted prep plan for ML interviews. Preparation should be intentional, role-specific, and company-informed.**

- **Create a tailored study plan** based on your target role. Spend 1–2 weeks on behavioral prep, overlap topics wisely, and prioritize deliberate, focused practice.
- **Do company-specific preparation** by researching their tech stack, ML frameworks, and use cases. Read blogs, papers, and talk to current employees to understand their challenges and how they approach ML.
- **For product-focused ML roles**, focus on business impact, case studies, model selection trade-offs, and deep feature engineering—interviewers value end-to-end thinkers.
- **For infrastructure-focused ML roles**, focus on scalable systems, distributed training, MLOps best practices, feature stores, and real-world reliability scenarios like traffic spikes.
- **For research-oriented ML roles**, go deep into recent papers, experimental design, evaluation strategies, and be ready to explain your own contributions with clarity—even to non-experts.

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/>

Mock Interviews Are Non-Negotiable

To wrap up our preparation strategy, we dive into how to *structure* our prep effectively—because randomly studying “a bit of everything” doesn’t work.

- **Mock interviews are non-negotiable**: They’re the fastest way to sharpen your skills, build confidence, and improve how you communicate under pressure.
- **Avoid practicing in real interviews**: We should use mocks to make mistakes and learn, not high-stakes opportunities.
- **Find partners in your network or online**: Leverage communities or platforms like ADP List to schedule mock sessions with others.
- **Record your mocks**: Watching ourselves afterward reveals blind spots we may not notice in the moment—this step alone can lead to major improvements.

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

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/>

Project Presentation

Some companies include a **project presentation round** in their ML interview process, where we’re expected to walk interviewers through a real project from start to finish. This is a powerful opportunity to showcase our end-to-end technical skills, communication ability, and strategic thinking.

- **Use visuals** to anchor the conversation—simple diagrams in Excalidraw or Google Docs help explain architecture clearly and keep interviewers engaged.
- **Explain concepts like you’re teaching a junior engineer**, avoiding jargon and offering quick context before diving into complex ideas (e.g., why negative sampling is needed).
- **Stay composed during interruptions**, as these are often tests of our communication clarity rather than signs of concern.
- **Show real trade-off thinking**—don’t just name-drop methods; explain *why* one approach was selected over others based on constraints like latency or scalability.

This round is about more than technical depth—it’s about demonstrating that we can **lead projects, make informed decisions, and communicate them with confidence and clarity**.

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 STAR+R Method For ML Experiences

In this segment, we explore how to present our machine learning project during a behavioral interview using an enhanced **STAR** method—with an extra **R** for **Reflection**. This approach helps us communicate our work clearly and effectively while demonstrating technical depth and self-awareness.

- **S – Situation**: We start by setting the context—what was the problem, when did it occur, and what were the constraints (e.g., latency limits, scale requirements, integration needs).
- **T – Task**: We define our role and specific goals. For example, improving a ranking model to boost session engagement by a measurable target.
- **A – Action**: We go deep on what we actually built. This includes model architecture choices (e.g., deep neural networks, two-tower systems), feature engineering, and design trade-offs. We explain why certain techniques were selected and how they fit into the production stack.
- **R – Result**: We share the measurable impact of our work—like a 2.2% engagement lift—and tie it to broader business or platform outcomes (e.g., full rollout after a successful A/B test).
- **R – Reflection**: Finally, we add a layer of introspection, discussing what we learned or would do differently next time—showing growth, humility, and systems thinking.

This STAR+R framework not only helps structure our storytelling but also leaves a lasting impression of us as thoughtful, impact-oriented ML professionals.

If you want to learn even more from Yayun:

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

Common ML Behavioral Questions

Here's a focused summary of the types of questions we should expect in the **ML behavioral and project deep dive interview round**, with an emphasis on communication, problem-solving, and cross-functional thinking.

- We’ll often be asked to **describe a challenging ML project**—this is our chance to walk through the problem, solution, and business impact in a structured story.
- Expect to explain **how we improved model performance**, including what metrics we tracked, the changes we made, and the quantifiable outcome.
- Be prepared to discuss **failures and setbacks**—interviewers want to hear how we responded, what we learned, and how we iterated or recovered.
- We’ll likely need to **explain ML concepts to non-technical stakeholders**, showcasing our ability to communicate complex ideas clearly and persuasively.
- For ML infrastructure roles, we should be ready to talk about **frameworks or tools we built**, the **scaling challenges** we solved, and how we enabled productivity for other engineers or data scientists.

In every answer, we should highlight not just *what* we did, but *how* we thought, the **trade-offs** we considered, and **what we learned**.

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 ML Behavioral

Here’s a summary of what we should expect and aim to convey in the **Project Deep Dive and Behavioral Interview Round**—a conversational but highly valuable part of the ML interview process.

- We’re expected to go beyond surface-level descriptions and **deeply explain a past ML project**, including the problem, our solution, and the technical trade-offs we made.
- This is our chance to show **real-world impact**, how we debugged challenges, optimized performance, or iterated on feedback.
- For **ML infrastructure roles**, we should be ready to discuss tools or systems we built (e.g. training pipelines, feature stores), and how they enabled or scaled team productivity.
- Strong performance in this round depends on **clarity, structure, and storytelling**—connecting technical depth with our thought process and decision-making.
- Ultimately, it’s about showing that we not only *built* things, but thought critically and communicated effectively through the process.

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/>

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/>

System Design Example: TikTok Recommendation System

Here’s a summary of the **infrastructure-focused ML system design** walkthrough. We are building a robust, scalable recommendation platform to support multiple product teams, focusing on infrastructure needs such as latency, reliability, and modularity.

- **We design for scalability and flexibility**, supporting 10M+ daily predictions across various surfaces (e.g., homepage, search, email), with &lt;50ms latency for real-time use cases and batch support for offline use.
- **We architect the system** with modular components: batch and streaming feature pipelines, a centralized feature store, a model training platform, a model registry, and scalable inference servers, all integrated with monitoring and alerting.
- **We implement tiered feature computation** (long-term, medium-term, real-time) using tools like Spark and Flink, and store user, item, and contextual features consistently to power accurate and fresh predictions.
- **We support multiple modeling strategies**, including two-tower models, matrix factorization, and DNNs, and use a two-stage architecture with candidate generation followed by precise ranking.
- **We deploy models reliably** using CI/CD pipelines, blue-green deployments, and shadow testing, and maintain performance through A/B testing, autoscaling servers, inference optimizations, and drift detection.

This approach ensures we build ML systems that are production-ready, highly available, and adaptable to the evolving needs of diverse 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/>

In this example, we apply the **5-part ML system design framework** to a product-focused recommendation system, like the “For You” page on TikTok. This walkthrough shows how to translate theory into a practical, high-impact system that balances user experience, business goals, and infrastructure at scale.

- **Problem Navigation**: We identify key functional and non-functional requirements—personalized video recommendations under 100ms latency for over a billion users. Product goals include maximizing watch time, retention, and creator growth, while supporting real-time responsiveness.

- **High-Level Design**: The system includes a content ingestion pipeline, feature extraction services, candidate generation and ranking models, a real-time serving layer, and a feedback loop. We distinguish between components that require real-time updates (e.g., inference) and those that can run in batch (e.g., daily retraining).

- **Data Strategy & Feature Engineering**: We engineer rich **user features** (behavioral, demographic), **video features** (metadata, embeddings), and **contextual features** (session state, time of day). Concrete examples include a **creator consistency score** and a **video novelty score**.

- **Modeling Approach**: We use a two-stage architecture:

  - **Candidate generation**: Options include two-tower models, matrix factorization, neural collaborative filtering, and graph-based models.

  - **Ranking**: GBDTs for interpretability or deep models for richer signal capture. We also plan for cold start solutions and use exploration strategies to mitigate popularity bias.

- **Evaluation, Deployment & Monitoring**: We evaluate offline with AUC and DCG, and run A/B tests in production to track watch time and session length. To meet latency goals, we apply compression, caching, and quantization. Monitoring covers drift, degradation, and business KPIs—with real user feedback continuously improving the system.

This structured approach helps us communicate clearly, think systemically, and demonstrate readiness for designing real-world ML systems at scale.

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 ML System Design

In this segment, we’re introduced to one of the most critical and career-defining parts of the ML interview process: **system design**. Especially at senior and staff levels, this round reveals our ability to lead and think holistically.

- **System design interviews evaluate both breadth and depth**—we’re expected to understand the full ML pipeline from data ingestion to deployment and monitoring.
- This round is less about solving isolated problems and more about **architecting end-to-end solutions** that operate under real-world constraints.
- We must demonstrate our ability to **make trade-offs** involving scalability, latency, noisy data, evolving product requirements, and long-term maintenance.
- Strong performance here often **distinguishes mid-level candidates from senior or leadership-level hires**.
- Ultimately, it’s our chance to show that we can **design ML systems that work, scale, and deliver value in production—not just in theory**.

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/>

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/>

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.

Gilad’s Take: Missed Estimates

Missed Estimates

Gilad’s Take: Motivated IC Auto-Didact

Motivated IC Auto-Didact

What Do Managers Do?

Final Thoughts

Challenge Yourself, Be Authentic

Leverage Your Allies In The Process

Creating A Customized Study Plan

Mock Interviews Are Non-Negotiable

Level-Specific Expectations

Project Presentation

The STAR+R Method For ML Experiences

Common ML Behavioral Questions

Round Structure ML Behavioral

Level-Specific Expectations

System Design Example: TikTok Recommendation System

System Design Example: TikTok Recommendation System

Round Structure ML System Design

Preparation Strategy