Amazon Behavioral Interview [Part 17] - Handling Unclear Expectations From Leadership (Answer)

This video segment describes a situation where the interviewee faced conflicting demands from leadership: the need to drive customer satisfaction and improve resolution times while also maintaining employee engagement and retention.

Here are the key takeaways:

- **Identified the Conflict:** The interviewee recognized the conflicting demands from leadership regarding customer satisfaction metrics and employee well-being, particularly during a challenging period with COVID-19.
- **Navigated the Challenges:**
  - **Open Communication:** They fostered open communication with their team, actively seeking feedback and addressing concerns.
  - **Prioritized Employee Well-being:** Despite the pressure to meet performance metrics, they prioritized employee well-being by:
    - Implementing measures to reduce stress, such as virtual happy hours and flexible work arrangements.
    - Actively listening to employee feedback and addressing their concerns.
  - **Balanced Priorities:** They successfully navigated the competing demands by finding a balance between meeting performance targets and maintaining employee morale.
- **Demonstrated Leadership:** The interviewee demonstrated strong leadership by:
  - Proactively addressing the challenges and seeking solutions.
  - Effectively communicating expectations and priorities to their team.
  - Building trust and fostering a supportive team environment.

This example highlights the interviewee's ability to navigate complex situations, prioritize competing demands, and effectively lead and motivate their team.

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

How Managers Hire Great Engineers [Part 12] - Outreach Basics

So you've found a great candidate - Now it's time to get them into your interview loop. From this lesson, you will:

- Understand the benchmark for a good response rate when sourcing candidates, with 15% as the target for moving candidates forward in the recruitment process
- Learn that email outreaches typically yield higher response rates than LinkedIn InMails and why finding a candidate’s personal email can be more effective
- Discover the advantages of outreach coming directly from an engineering manager rather than a recruiter to significantly increase the likelihood of a response
- Explore tools and methods for finding candidate emails, including using services like Clearbit and experimenting with Gmail to predict email addresses
- Gain insights into how personalizing the outreach process by handling initial replies and interactions can make a meaningful impact on recruitment outcomes

Connect with John Egan on LinkedIn: [https://www.linkedin.com/in/jwegan/​](https://www.linkedin.com/in/jwegan/%E2%80%8B)

Paper Reading - Attention is All You Need

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

"Attention is All You Need" is a seminal research paper published by researchers at Google in 2017, introducing the Transformer model. The paper revolutionized natural language processing and machine translation by proposing a new architecture that relies solely on self-attention mechanisms, eliminating the need for recurrent or convolutional layers.

This paper and the Transformer model it introduced are crucial in the context of Large Language Models (LLMs) for several reasons:

- Parallelization
- Long-Range Dependencies
- Efficient Attention
- Mechanism Pre-training and Transfer Learning

We will be discussing the internals of the Transformer architecture which will help guide our journey into the LLM space.

Paper link - <https://arxiv.org/abs/1706.03762?ref=blog.oxen.ai>\
Event link - <https://www.jointaro.com/event/paper-reading-attention-is-all-you-need/>

Amazon Behavioral Interview [Part 11] - Tell Me About A Time You Exceeded Expectations (Answer)

This video segment describes the interviewee's experience successfully increasing the acquisition of a business credit card at Costco.

- **Identifying an Opportunity:** The interviewee recognized the untapped potential of the business card, which offered higher profitability compared to the consumer card. They also identified a key differentiator: a higher gas rebate for the business card.
- **Leveraging Internal and External Factors:** The interviewee capitalized on the increased warehouse incentive for selling business cards and secured additional funding for training and in-store marketing.
- **Overcoming Challenges:**
  - **Internal Competition:** The interviewee faced resistance from the consumer card division, who perceived the increased business card acquisition as cannibalizing their own customer base.
  - **External Constraints:** The interviewee navigated challenges related to Costco's limited marketing space and restrictions on in-store promotions.
- **Significant Results:** Despite these challenges, the interviewee successfully tripled the initial acquisition target, demonstrating strong project management and execution skills.

This example highlights the interviewee's ability to identify and capitalize on opportunities, overcome internal and external challenges, and achieve significant results.

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

Politics

As companies scale, politics become inevitable - Big Tech companies are no exception. While these environments offer great compensation and talent density, navigating organizational complexity and interpersonal dynamics becomes a critical part of engineering success.

- **Politics Are Inevitable in Large Orgs**: As tech companies grow, layers of leadership and cross-functional dependencies make decision-making complex. Engineers must often align with numerous stakeholders, especially when aiming for senior or staff roles.
- **Shipping Requires Consensus-Building**: Even seemingly minor changes can require significant internal negotiation. One example cited involved a six-month project to enlarge a button being blocked just before launch due to last-minute leadership disagreement.
- **People Over Code**: The technical work is often less challenging than influencing and winning over colleagues who may disagree or have competing priorities.
- **Burnout from Frustrated Efforts**: Senior engineers can burn out after investing months into initiatives that get shut down due to misaligned incentives or lack of political foresight.
- **Success Requires “Playing the Game”**: Engineers who are averse to navigating internal politics may struggle, especially in high-visibility roles. Building relationships proactively is often essential for seeing projects through.

The AI Frontier: Get Paid To Build Smarter LLMs

The future of AI is being built by a handful of companies: OpenAI, Anthropic, and DeepMind, among others. One of the core ingredients for improving the performance of LLMs is the use of reinforcement learning from human feedback (RLHF).

Join us for a live conversation with Mercor's cofounder and CTO, [**Adarsh Hiremath**](https://www.linkedin.com/in/adarsh-h-623941204) hosted by Taro's cofounder and CEO, [**Rahul Pandey**](https://www.linkedin.com/in/rpandey1234/). Mercor is a key partner to enable RLHF for many frontier model companies. As part of this, Mercor offers 1000s of part-time, remote-friendly roles with competitive compensation.

This is a new type of white-collar work that involves debugging, validating, and creating scenarios for LLMs. As the capabilities of models improve, the expertise required to train models keeps increasing. One of the highest-priority areas is around coding, which means Software Engineers have a special role to play in this type of work.

This event explains the pros and cons of AI lab gig work, what exactly the work entails, and how much you can earn.

Some topics we’ll cover:

- Typical hourly rates and the compensation model
- Project-to-project variability
- Unique challenges of remote collaboration in this type of work
- What does the day-to-day look like with this type of work?
- How does this work impact the development of LLMs and AI agents?
- How will this work evolve over the next 5 years?

There will also be time for open Q&A from the audience.

Mercor is an AI-powered recruiting platform that has become one of the largest sources for human labor for foundation model companies. Mercor has raised more than $100 million dollars and is located in San Francisco, CA. Explore [**relevant jobs on Mercor here**](https://work.mercor.com/?referralCode=91aab11f-ce55-40b3-9c2d-f74576cac28f) (100+ Software Engineering jobs available).

How Managers Hire Great Engineers [Part 21] - Level-Specific Info

Hiring great engineers gets exponentially harder the more senior the headcount you are looking for. From this lesson, you will:

- Understand the need to adjust sourcing tactics for different engineering levels, such as using root sourcing primarily for more seasoned engineers (L6 and above).
- Learn that junior roles (L3, L4) may not benefit as much from certain tactics like source-a-thons due to senior engineers typically not having extensive networks of junior engineers.
- Recognize that closing efforts must be scaled according to the candidate's level, with more senior candidates (L7 and above) requiring more extensive efforts to secure their acceptance.

Connect with John Egan on LinkedIn: <https://www.linkedin.com/in/jwegan/>

What Should I Do In The First 10 Minutes?

In this lesson we talk about how to strategically approach the first 10 minutes of an AI-assisted coding interview to set yourself up for success.

- **Resist the urge to dive in immediately** — instead of jumping straight into coding or prompting the AI, start by restating the problem in your own words and doing a strategic skim of the codebase to understand existing functionality and function signatures.
- **Uncover hidden requirements** — as you explore the code, actively look for things like bot user filtering, non-active event handling, or data normalization that may not have been spelled out in the prompt, and clarify these with your interviewer.
- **Turn requirements into tests early** — unlike traditional interviews, convert each discovered requirement into an executable test case upfront (using AI to help generate them if needed), since verifying AI-generated code is a core part of the assessment.

Become An Engineer Influencer [Part 12] - Engineering Topics

What Makes A Good Growth Environment For Promotion

Which of the following is a great way to increase your scope?

True or false: It is good to work in a company where there are constantly too many problems to solve.

Why is there always a plentiful supply of problems to solve in high-growth tech companies?

Amazon Behavioral Interview [Part 13] - Delivering Results (Answer)

In this video, the speaker describes a situation where they had to implement a new process quickly to improve internal communication and efficiency. Here are the key takeaways in 3 bullet points:

- **Identified a Problem:** The speaker's team was facing issues due to multiple channels for internal stakeholders to submit escalations. This created confusion and made it difficult to track issues.
- **Implemented a Solution:** The speaker quickly developed a plan to create a single point of entry (\[email address removed\]) for all escalations. This streamlined the process and ensured that issues were not falling through the cracks.
- **Achieved Results:** The new process was successfully launched within a tight deadline. While there was some initial resistance from some stakeholders, the speaker was able to address concerns and ultimately achieve nearly 100% compliance.

Overall, the speaker's experience highlights the importance of being adaptable and resourceful in a fast-paced work environment. It also demonstrates the value of clear communication and collaboration in achieving successful outcomes.

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

When Should I Actually Use AI During The Interview?

In this lesson we talk about when and how to use AI effectively during an AI-assisted coding interview.

- **Find the right balance with AI:** Learn how to avoid over-delegating core logic to AI or underusing it and running out of time, by aiming for the “middle path” that maximizes leverage while minimizing risk.
- **Accelerate typing, not thinking:** Discover why you should use AI for scaffolding, boilerplate, and clearly defined test cases—but retain ownership over algorithms, data structures, and key design decisions.
- **Optimize for easy verification:** Understand that AI is most helpful when you already know what needs to be built and can quickly verify the output, and least helpful when you’re still figuring out the correct approach.

SWE Internship Survival Guide [Part 19] - The Real Treasure

Internships are a unique chance to grow your network alongside your technical skills. Here are the core points from the lesson:

- The other interns you work with often become valuable long-term connections—professionally and personally
- Shared experiences during an internship create natural bonding opportunities, so don’t miss out by being overly heads-down
- Building your network isn’t just about being social—it’s also about doing great work and being helpful to others

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/)

Taro Book Club: The Fundamentals of Data Engineering - Chapter 3 (Part 2)

Session led by [Gideon Blinick](https://www.linkedin.com/in/gideonblinick/)

Chapter 3 - Part 2: Designing Good Data Architecture

In our first session on Chapter 3, we learned the 9 principles of Good Data Architecture, which are largely based on Google's Principles of Cloud-Native Architecture and Amazon's Well-Architectured Framework.

Now we get less abstract and cover Major Architecture Concepts and Examples, including Domains and Services, Distributed Systems, Data Warehouses and Lakes, the Modern Data Stack, and so much more!

Where's The Scope?

Here are the core points from the lesson:

- **Too Many Smart Engineers, Too Few Big Problems Left** - Decades of optimization have left many core products (like Facebook Home Feed) nearly maxed out, making impactful changes rare and promotions harder to earn.
- **Engineers Flee Mature Teams in Search of Scope** - Engineers often transfer to newer initiatives (e.g., Meta’s Portal) to find unclaimed problems they can own—especially critical in systems with forced promotion deadlines.
- **Forced Promotion Policies Increase Pressure** - At Meta for instance, engineers must be promoted from mid-level (E4) to senior (E5) within five years or face termination, driving a scramble for high-impact work. This is awkward when there's a profound lack of scope.
- **Promotion Bottlenecks Are Common** - Transitions like Amazon SD2 → SD3 or Google L4 → L5 are notorious for being highly competitive, political, and frustrating due to overlapping ambitions.
- **Scarcity of Senior/Staff-Level Scope** - Teams without senior-level problems to solve—or ones where existing staff already own them—make it nearly impossible for newer engineers to grow beyond a certain level.

How Managers Hire Great Engineers [Part 8] - School Websites

One of the biggest predictors of engineer quality is the university they went to. From this lesson, you will:

- Learn how to use Google and school directories to find potential candidates, particularly for new grad PhDs or specialized fields like machine learning
- Discover how to locate personal web pages of grad students and alumni by using targeted search queries, such as searching for specific domains like “site:[stanford.edu](http://stanford.edu)”
- Understand the advantage of sourcing candidates through school websites as a way to reach individuals who are less inundated with recruiting contacts compared to those on LinkedIn

Connect with John Egan on LinkedIn: [https://www.linkedin.com/in/jwegan/​](https://www.linkedin.com/in/jwegan/%E2%80%8B)

Hi, I’m Alex

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, I introduce myself and share my mission to help tech leaders thrive, which is a passion of mine.

- I have over 18 years of experience in the tech industry, including more than 8 years in leadership roles, and I graduated with a bachelor's in software engineering from the University of Waterloo.
- My goal is to assist individuals in becoming better versions of themselves through effective leadership. I encourage viewers to engage with me on this journey of growth and development.

The Case AGAINST Big Tech

We discuss reasons why Big Tech may not be the right fit for everyone, especially as career goals and personal priorities evolve over time.

- **High Work-Life Balance Needs**: For those with growing personal commitments—like family, health, or other priorities—Big Tech’s demanding environment may no longer be sustainable or fulfilling.
- **Low Tolerance for Pressure**: The performance-driven, competitive nature of Big Tech can be overwhelming for those who prefer a calmer, less intense work atmosphere.
- **Preference for Independent, Heads-Down Work**: Engineers who thrive in solo coding environments without heavy documentation, collaboration, or politics may find the collaborative and process-heavy style of Big Tech frustrating.
- **Avoidance of Office Politics**: Success often requires political navigation, influence-building, and strategic communication—elements that don't appeal to everyone.
- **Desire for Simplicity**: Not everyone aspires to climb the corporate ladder; for those who just want to write good code without excessive overhead, there are better environments than Big Tech.

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/>

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.