Taking Things Further

In Part 4 of the course, we focus on how to **embed "thinking in deltas" as a daily leadership habit**. This final module emphasizes how to continuously spot and create positive, sustainable change as a manager—not just react to problems.

- **We build the habit by journaling our daily deltas**, capturing the tangible changes we influence, however small.
- **We use the journal to reframe how we think**, making it easier over time to spot opportunities for impact in individuals, teams, and systems.
- **We improve our mental well-being as managers** by recognizing our contributions and wins in a role where feedback loops are long and ambiguous.
- **We measure our success not just by outputs, but by meaningful changes we've catalyzed**—deltas we can point to as outcomes of our leadership.

Creating a daily "delta journal" isn’t just about reflection—it’s how we grow our impact and resilience as leaders.

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: Burnt Out and Behind

In Part 3 of the course, we explored how to apply the core lesson from Part 2—creating positive and sustainable change in organizational performance—through four practical exercises. Each scenario challenged us to identify the **"deltas"** we can introduce as managers to increase impact at the individual, team, or systems level.

- **We learned to recognize that not all growth is credited to us**, and we must focus on the growth that happens *because* of our involvement—not despite it.
- **We explored supporting high performers** by making their growth more efficient (faster) and effective (more aligned with goals), rather than just letting it happen passively.
- **We tackled underperformance** by identifying root causes, offering tailored support, and protecting teammates from negative ripple effects.
- **We addressed silent group dynamics** by recognizing the power of culture and investing in small, consistent changes to build psychological safety and lasting team habits.
- **We emphasized ruthless prioritization** as a powerful delta: helping teams focus on what truly matters, not just work harder—transforming effort into sustainable outcomes.

Now, we’re ready to take this impact to the next level in Part 4.

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)

Burnt Out and Behind

In this final exercise, we confront a situation where a team is putting in extra hours but still failing to meet deadlines. This signals systemic issues that go beyond individual effort—and calls for thoughtful intervention.

- We recognize that **unsustainable effort without results is a red flag**—it shows that working harder isn't the solution.
- We can introduce deltas by **investigating root causes**: Are goals unrealistic? Are priorities misaligned? Is there a process breakdown?
- It’s our responsibility to **shift focus from intensity to effectiveness**—fixing the system so performance improves without burnout.
- We look at **team norms, project planning habits, and cross-functional dependencies** to identify leverage points for change.
- Our role is to help the team **recover sustainably**, improving delivery while preserving morale and long-term capacity.

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: System Design Meeting Non-Participation

In this reflection on team participation during design reviews, we explored how group dynamics can have a powerful influence beyond the individuals themselves. The challenge highlights the importance of managing team culture and fostering an environment where all voices are heard.

- We acknowledge that **group behaviors can persist even as individual team members change**—a reminder that team culture is distinct and durable.
- Our role is to **introduce small, consistent deltas** that collectively improve psychological safety and encourage open participation.
- We focus on **building psychological safety** by recognizing barriers to contribution and intentionally working to lower them.
- Though these efforts may seem small or slow, **they compound into deep, sustainable cultural change** that supports long-term performance.
- Strong team cultures—like ones where everyone contributes—require persistent effort, but the payoff is a **resilient and high-performing team**.

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)

Foundational Ideas

In this key section of the course, we dive into the essential machine learning concepts that consistently appear across all types of ML interviews. This foundation prepares us to explain not just the “what,” but the “why” behind our choices—something interviewers value highly.

- We review core **ML paradigms** like supervised, unsupervised, and reinforcement learning, along with real-world examples to explain their use cases clearly.
- We explore **machine learning tasks** such as classification and regression, and when to apply specific evaluation metrics like precision, recall, or RMSE.
- We gain a deep understanding of **model performance** concepts—bias/variance trade-off, underfitting/overfitting—and how to recognize and address them.
- We cover **validation strategies**, data leakage prevention, and the right use of metrics beyond just accuracy, such as AUC-ROC and F1 score.
- We enhance our skills in **feature engineering**, **optimization algorithms** (like SGD and Adam), **model selection reasoning**, and **dimensionality reduction** techniques like PCA and UMAP.
- We also touch on **LLM fundamentals**—such as Transformers and encoder-decoder architectures—to stay current with emerging trends.
- Most importantly, we practice explaining all of this in a way that connects to real-world problems and highlights our decision-making skills.

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/>

Course Roadmap

In this part of the course, we’re given a roadmap that outlines the full journey of preparing for machine learning interviews. Each section is designed to build our skills systematically and strategically, based on real-world experience and mentorship insights.

- We start with **essential ML concepts**, learning how to clearly explain high-frequency topics that appear in nearly every interview.
- We then move into **practical ML coding and modeling**, covering notebooks, EDA, model selection, feature engineering, and evaluation techniques.
- **Coding foundations** are reinforced to help us succeed in general algorithm rounds, with a focus on what companies expect from ML candidates.
- We’ll tackle **ML algorithm coding**, where we may be asked to implement algorithms like decision trees or k-means from scratch.
- **ML system design** is emphasized for senior-level roles, with frameworks and examples for building scalable systems.
- Finally, we prepare for **project deep dives and behavioral interviews**, learning how to share impactful stories, followed by crafting a personalized **study plan** that fits our timeline and goals.

If you want to learn even more from Yayun:

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

Before you challenge them, ask if they trust you yet.

You’ve seen this happen.\
A good idea, pushed too soon—and everything shuts down.

This section gives you the signal to look for before you push.\
And what to do if it’s not there yet.

**Want to go deeper?**

- Explore more tools and courses at [**Taha’s Method**](https://www.tahasmethod.com/) — from interview prep to leadership development.

- Follow me on [**LinkedIn**](https://www.linkedin.com/in/tahahussain/) and subscribe to my weekly newsletter, [**The Conscious Leader**](https://tahahussain.substack.com/), for more lessons like these.

- Get 30% off [**Top Tech Leader**](https://bit.ly/tarotopleader) with code **ILOVETARO** (exclusive for Taro members).

You listened. But not to what mattered.

It’s possible to replay the entire meeting in your head and still miss the thing they needed you to hear.

This isn’t about listening better.\
It’s about listening for something else entirely.

**Want to go deeper?**

- Explore more tools and courses at [**Taha’s Method**](https://www.tahasmethod.com/) — from interview prep to leadership development.

- Follow me on [**LinkedIn**](https://www.linkedin.com/in/tahahussain/) and subscribe to my weekly newsletter, [**The Conscious Leader**](https://tahahussain.substack.com/), for more lessons like these.

- Get 30% off [**Top Tech Leader**](https://bit.ly/tarotopleader) with code **ILOVETARO** (exclusive for Taro members).

They’re not resisting change. They’re protecting something.

It’s easy to think they’re pushing back just to be difficult.\
But more often, they’re trying to keep something from breaking.

This section slows the moment down.\
Just enough to ask: what might they lose if they say yes?

**Want to go deeper?**

- Explore more tools and courses at [**Taha’s Method**](https://www.tahasmethod.com/) — from interview prep to leadership development.

- Follow me on [**LinkedIn**](https://www.linkedin.com/in/tahahussain/) and subscribe to my weekly newsletter, [**The Conscious Leader**](https://tahahussain.substack.com/), for more lessons like these.

- Get 30% off [**Top Tech Leader**](https://bit.ly/tarotopleader) with code **ILOVETARO** (exclusive for Taro members).

Maximize Your Equity And Benefits In Tech [Part 3] - Foundational Tax Concepts

Understanding how taxes work is key to making smart decisions about equity compensation—here are the core points from the lesson:

- Gross income is reduced by payroll taxes and deductions to determine adjusted gross income (AGI) and taxable income, which drives your federal tax liability
- Long-term capital gains (assets held over a year) receive lower federal tax rates than ordinary income, often saving high earners significant amounts
- State tax treatment varies—most states don’t offer preferential rates for long-term gains, and some, like Massachusetts, penalize short-term gains more heavily
- Investment losses can offset gains and up to $3,000 of ordinary income per year, but “wash sale” rules can disallow losses if you repurchase too soon
- The Alternative Minimum Tax (AMT) may kick in if you're using lots of deductions or exercising incentive stock options, so it’s important to model this in advance

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/)

Build Deep Relationships Quickly In Tech [Part 11] - Identifying Fit

Some people simply aren't meant to get along, and you will run into a lot of these people across your career. This lesson teaches you:

- Why finding right fit connections is so important for networking, particularly with your efficiency
- How professional networking really isn't that different from making friends "in real life" (IRL)
- Solidarity angles you can leverage to find good fit connections

SWE Internship Survival Guide [Part 16] - Diversify Your Mentors

As you approach the end of your internship, one of the most important factors in getting a return offer is how you’re evaluated during your performance review. This lesson focuses on how to set yourself up for success by building strong relationships across your team. Here are the core points from the lesson:

- Don’t rely solely on your assigned intern mentor—build relationships with multiple teammates to avoid single points of failure
- You’ll receive 360 feedback at the end of your internship, so proactively engage with others who can speak positively about your work
- Identify 2–3 potential advocates by week two and keep them updated through code reviews, one-on-ones, or casual check-ins

Learn how to build relationships here: [\[Masterclass\] How To Build Deep Relationships Quickly In Tech](https://www.jointaro.com/lesson/tyaPFMXPoz2pmzB7OVzt/masterclass-how-to-build-deep-relationships-quickly-in-tech/)

SWE Internship Survival Guide [Part 11] - Code Velocity

Your success as a software engineering intern hinges on one key metric: your ability to ship code consistently. Here are the core points from the lesson:

- Code velocity is the primary way interns are evaluated, so focus your time on writing and shipping impactful code
- Every company has a different pace, but you should ask your manager for benchmarks and observe full-time engineers to calibrate your own output
- Structure your day around long blocks of uninterrupted coding time, and avoid distractions like intern socials or long-term architecture meetings
- Make small, frequent code changes instead of large ones to make code review easier, allow quicker course correction, and prevent painful merge conflicts
- Communicate your approach with your team so they understand your intent to seek faster feedback through smaller pull requests

SWE Internship Survival Guide [Part 4] - How Internships Work At Top Companies

Top-tier internships at companies like Meta or Google are structured, high-impact opportunities that can transform you into a confident, job-ready new grad. Here are the core points from this lesson:

- Legitimate internships at top companies have clear structure—interns are given managers, mentors, well-scoped projects, and timelines
- Interns are treated like junior engineers, often contributing to real production work and collaborating closely with full-time teammates
- Larger companies offer more formal evaluation processes for return offers, which helps reduce ambiguity and stress compared to smaller, less organized internships
- You’ll likely focus on one or two substantial projects to avoid context switching and to enable deeper, more meaningful contributions
- Adopt a collaborative mindset with fellow interns—success isn’t zero-sum, and sharing knowledge will help everyone grow and build stronger networks

Ship & Grow 500k+ User Side Projects [Part 6] - Least Clicks Design

You don't need to be a product design to build great software with a smooth user interface. This lesson teaches you:

- Why *every* engineer needs to care about UX, not just front-end engineers (web/mobile)
- The core philosophy you need to remember when crafting user interfaces
- The difference between a good product and a bad product when it comes to least clicks design
- What it looks like to truly champion least clicks design, explained with an in-depth example

Ace The ML System Design Interview [Part 3] - Big Picture Design

A strong big-picture system design is crucial for success in machine learning system design interviews. It's not about drawing a perfect diagram immediately, but about demonstrating an understanding of the system's components and their interactions. Here are the core points from this lesson:

- Identify the inputs and outputs of the system during the alignment phase to establish a foundation for building the diagram
- Start with the user and the service, then progressively add components, especially those involving machine learning, to illustrate the flow of data and processes
- Break down machine learning components beyond a single "ML system" box to show the different stages and types of models involved
- Include essential infrastructure elements, such as feature stores, to demonstrate a comprehensive understanding of the system
- Iterate on the diagram, adding or modifying components as a deeper understanding of the problem and its requirements develops

If you like what Ilya has to say, subscribe to his YouTube for more high-quality ML/AI career guidance: [MLEpath - YouTube](https://go.mlepath.com/taro-youtube-mlepath)

If you want hands-on support from Ilya to crack the FAANG ML interview, join his coaching program: [MLEpath - Coaching Program](https://go.mlepath.com/taro-march-25)

Paper Reading: DeepSeek R1 - Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Session led by [Mike A](https://www.linkedin.com/in/malik-mike-a-32b63818b/) 

Join us for an insightful session on the groundbreaking paper, DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. In this session, we will explore DeepSeek-R1, a state-of-the-art reasoning model that pushes the boundaries of reinforcement learning (RL) applied to large language models (LLMs). 

This paper introduces two novel models, DeepSeek-R1-Zero and DeepSeek-R1, showcasing advancements in reasoning capabilities through RL-driven self-evolution. Unlike traditional models relying heavily on supervised fine-tuning (SFT), DeepSeek-R1-Zero develops its reasoning abilities purely through RL, while DeepSeek-R1 combines RL with a multi-stage training pipeline for enhanced performance. 

The paper highlights exceptional benchmarks achieved by DeepSeek-R1 on math, coding, and STEM-related reasoning tasks, where its performance rivals that of leading closed-source models such as OpenAI's o1-1217. Additionally, we’ll discuss distilling these capabilities into smaller, more efficient models to make advanced reasoning accessible for diverse applications. 

Whether you're a researcher, developer, or enthusiast in AI and LLMs, this paper reading will provide an in-depth understanding of the novel reinforcement learning techniques driving DeepSeek-R1 and its implications for the future of AI-driven reasoning systems. Don't miss this opportunity to engage with cutting-edge advancements in the field! \[2501.12948\] DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning 

GitHub - deepseek-ai/DeepSeek-R1 

Mark Chen X Post: <https://x.com/markchen90/status/1884303237186216272?s=12&t=BSlMYtugAr8LLWXLnMLLrQ> 

GRPO Diagram: <https://www.reddit.com/r/LocalLLaMA/comments/1i78sfs/deepseek_r1_grpo_code_open_sourced/#lightbox> 

The Bitter Lesson: <http://www.incompleteideas.net/IncIdeas/BitterLesson.html> 

Gradient Descent Image: <https://miro.medium.com/v2/format:webp/1\\\*f9a162GhpMbiTVTAua_lLQ.png> 

V3: <https://arxiv.org/abs/2412.19437> 

DeepSeek Math: <https://arxiv.org/abs/2402.03300> 

rstar-math: <https://arxiv.org/abs/2501.04519> 

Verify Step-by-Step: <https://arxiv.org/abs/2305.20050> 

DeepSeek catch-up chart: <https://media.licdn.com/dms/image/v2/D4E22AQG552O63UNJWg/feedshare-shrink_800/B4EZTN\\\[…\\\]41824000&v=beta&t=ubyKQniaCJTL37PzIOJi9YZRo1AF8yipuauSioyn59U> 

UC Berkeley Student $30 replication of Aha: <https://x.com/jiayi_pirate/status/1882839370505621655> 

Deep Agent R1-V replication: <https://x.com/liangchen5518/status/1886171667522842856> 

s1: <https://arxiv.org/abs/2501.19393> 

DeepSeek-r1 cost breakdown: <https://semianalysis.com/2025/01/31/deepseek-debates/> 

R1 Deep Dive: <https://fireworks.ai/blog/deepseek-r1-deepdive> 

All in Podcast: <https://podcasts.apple.com/us/podcast/all-in-with-chamath-jason-sacks-friedberg/id1502871393?i=1000687638652> 

Peter Gostev LinkedIn: <https://www.linkedin.com/in/peter-gostev/> 

LLM Agents Learning: <https://llmagents-learning.org/sp25>

Engineer Your Ideal Tech Career [Part 14] - Defining Near-Term Goals

Moving into your last section and with inspiration from the engineers you just heard from, you can expect to start defining near-term goals that you can implement within the next week, month, or quarter, to start charging towards your ideal long-term career.

This section will include tangible advice you can implement on the job or if you are preparing for interviews.

Looking for mentorship from tech’s top engineers + personalized job hunt assistance and interview prep? [Formation.dev](http://Formation.dev) can provide you unlimited career coaching, technical mentorship, personalized practice, and mock interviews. Join us here: <https://formation.dev/partners/taro>

Worksheet: [Formation - Coding Your Career](https://firebasestorage.googleapis.com/v0/b/tech-career-growth.appspot.com/o/courses%2Fslides%2FFormation%20Career%20Direction%20Worksheet.pdf?alt=media&token=f6344b78-529a-411e-a449-7a34a8d55fc8)

Amazon Behavioral Interview [Part 28] - Recovering From A Bad Decision (Evaluation)

This video segment provides feedback on an individual's response to a behavioral interview question about a time they made a bad decision.

- **Strengths:**

  - **Owned the Mistake:** The interviewee openly acknowledged the mistake and took responsibility for the negative consequences.
  - **Demonstrated Self-Awareness:** They identified the root cause of the mistake – focusing solely on short-term revenue gains while neglecting long-term customer value and potential.
  - **Highlighted Corrective Actions:** The interviewee described the steps taken to rectify the situation, such as revising the incentive structure, improving call center response times, and implementing measures to address long wait times.
  - **Learned from the Experience:** The interviewee outlined the key learnings from this experience, including the importance of considering long-term customer value and gathering input from stakeholders before implementing significant changes.

- **Areas for Improvement:**

  - **Quantify Impact:** The interviewee could have further quantified the impact of the mistake and the subsequent corrective actions. For example, they could have provided specific data on the improvement in customer satisfaction scores or the increase in customer retention rates after implementing the changes.
  - **Emphasize Positive Outcomes:** The interviewee could have further emphasized the positive outcomes of the project, such as the improved focus on customer lifetime value and the development of a more robust customer retention strategy.
  - **Connect to Amazon Leadership Principles:** The interviewee could have explicitly connected their actions and learnings to relevant Amazon Leadership Principles, such as "Customer Obsession," "Learn and Be Curious," and "Ownership."

- **Overall:**

  - The interviewee effectively demonstrated their ability to acknowledge and learn from mistakes, take responsibility for their actions, and implement corrective measures.
  - By incorporating the suggested improvements, the interviewee can further enhance their response and increase their chances of success in the interview.

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

Amazon Behavioral Interview [Part 4] - Tell Me About Yourself (Discussion)

This video segment discusses strategies for individuals who may not have the same level of prestigious work experience as those who have worked at Google, Facebook, or Salesforce.

- **Focus on Impact:** Instead of solely focusing on the names of companies, emphasize the impact you made in your previous roles. Quantify your achievements whenever possible (e.g., "increased revenue by 1400%").
- **Keep it Concise:** The "Tell me about yourself" section should be brief and impactful. Aim for a concise summary (around 3 minutes) to maintain the interviewer's attention.
- **Avoid Excessive Detail:** Steer clear of irrelevant details, such as personal anecdotes that are not directly related to your professional experience.
- **Prepare Thoughtful Questions:** Demonstrate your engagement and interest by preparing insightful questions to ask the interviewer.

By focusing on these points, individuals can effectively present their skills and experience, even without a background at major tech companies.

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.