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/)

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 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?

Overrated Career Goals

We explain which attributes engineers tend to over-prioritize and why doing so often leads to bad career decisions.

Here are the core points from the lesson:

- We see many early-career engineers chase trendy product spaces (ML, AR/VR, blockchain) without genuine passion, ignoring the high risk that trends fizzle and leave teams stagnant; survivorship bias makes the rare success stories seem more common than they are.
- We highlight a similar trap with technical stacks: engineers often assume “more modern is better,” jumping between new technologies and hurting long-term growth, when stable, proven stacks at strong companies typically offer better learning and career durability.
- We show that over-prioritizing level/title or short-term compensation can backfire, since inflated titles at weak companies and high pay tied to terrible work-life balance rarely lead to long-term career success or well-being; growth opportunities and supportive teams tend to drive compensation naturally over time.
- We stress that compensation is multi-variable (base vs. equity vs. upside potential), and examples like Barnes & Noble illustrate how deceptively high salaries may underperform compared to equity-rich tech roles over a multi-year horizon.

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/>

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)

Swingy Compensation

While Big Tech compensation packages can be extremely lucrative, they are also highly volatile. Much of the reward is tied to company stock, which fluctuates dramatically and is largely outside any individual engineer’s control. Here are the core points from the lesson:

- **Equity Makes Up the Majority of Pay at Senior Levels** - Compensation often includes base salary, a bonus, and a large portion in restricted stock units (RSUs), especially as engineers become more senior.
- **Stock Price Volatility Can Be Emotionally Draining** - Engineers may become fixated on stock movements - checking daily gains or losses that can translate to tens or hundreds of thousands of dollars in value swings.
- **Lack of Control Over Equity Value** - Company-wide factors like earnings calls or buyback announcements - rather than individual performance—drive major stock movements. A 26% drop in Meta's stock once erased $250 billion in market cap overnight.
- **Advice: Sell Regularly, Avoid Emotional Attachment** - Diversification and emotional detachment are key. Holding too much company stock creates psychological and financial vulnerability, especially in market downturns.
- **Stock-Based Wealth Is Largely Unpredictable** - Stock spikes and crashes often follow unexpected logic, making it nearly impossible for individual employees to anticipate or influence outcomes.

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!

Become An Engineer Influencer [Part 26] - Cohort Based Courses

Become An Engineer Influencer [Part 11] - Advice, Passion, and Build In Public

Become An Engineer Influencer [Part 9] - Community-First Approach

The Effect of Interest Rates

We walk through how macroeconomic conditions—especially interest rates—directly affect tech companies and tech workers.

- The federal funds rate guides how expensive it is for banks (and ultimately companies) to borrow money, and while near-zero rates felt normal recently, historically higher interest rates are actually the norm.
- When interest rates rise, the cost of borrowing increases, which makes companies far less willing to invest in long-term bets that may not pay off for several years.
- Because the tech industry is heavily driven by growth funded through borrowing and investment, higher rates trigger spending pullbacks, reduced hiring, and ultimately the contraction and layoffs we’re seeing across the market.

ML System Design Framework

In this segment, we’re introduced to a powerful **5-part framework** for approaching ML system design interviews. This structured method ensures we address both technical depth and strategic thinking—key traits interviewers look for in strong candidates.

- **1. Problem Navigation**: We start by asking clarifying questions to understand functional and non-functional requirements, product goals, infrastructure constraints, and user experience. We define success metrics, state our assumptions, and frame the scope clearly.
- **2. High-Level Design**: We map business goals to ML objectives, walk through core system components (data ingestion, processing, modeling, inference, monitoring), and identify what needs to run in real-time versus batch.
- **3. Data Strategy & Feature Engineering**: We explain how data is collected and labeled, ensure data quality, and outline our approach to creating, storing, and updating features—including how we handle feature freshness and online/offline processing.
- **4. Modeling Approach**: We present specific model choices with trade-offs in interpretability, scalability, and accuracy. We detail training methods (batch, online, hybrid), address data distribution shifts, and outline infrastructure needs.
- **5. Evaluation, Deployment & Monitoring**: We describe offline evaluation strategies, deployment infrastructure (latency, model size, caching), and our plan for monitoring model health (drift, degradation) while tying results back to business KPIs.

Using this framework helps us think like **system-level ML engineers**, ensuring we cover everything from high-level goals to production-readiness with clarity and confidence.

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/>

How Managers Hire Great Engineers [Part 9] - Source-a-thon

You don't need to only look outwards for candidates - They can often come from within, stemming from your current team! From this lesson, you will:

- Learn how to organize a Source-a-thon by creating a spreadsheet and having your team contribute potential candidates from their networks
- Understand the importance of warm outreach, including using a team member’s name for higher response rates
- Discover the benefits of conducting a Source-a-thon in a collaborative environment to generate more high-quality leads
- Realize the value of leveraging your team’s connections to source candidates who are likely to excel in the role based on previous work experiences

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

Amazon Behavioral Interview [Part 23] - Sacrificing Short-Term For Long-Term (Answer)

This video segment describes a situation where the interviewee had to make a difficult decision to sacrifice short-term gains for long-term value.

- **Identified the Problem:** The interviewee inherited a team with three regional teams operating independently, leading to inefficiencies and hindering the team's ability to scale and meet growing demands.
- **Made a Difficult Decision:** The interviewee made the difficult decision to transition the team from a regional model to a globally aligned model. This decision faced significant resistance from the team, leading to setbacks and the loss of some team members.
- **Overcame Challenges:** Despite the challenges, the interviewee successfully implemented the new model, resulting in significant improvements:
  - Reduced resolution times from seven hours to two hours.
  - Increased customer satisfaction (CSAT) significantly.
  - Improved operational efficiency and team productivity.
- **Learnings and Reflections:** The interviewee acknowledged that they could have improved their communication and change management strategies. They recognized the importance of more proactively communicating the need for change and the potential benefits of the new model to the team.

This example demonstrates the interviewee's ability to: 

- **Identify and address systemic issues:** They recognized the limitations of the existing system and proposed a solution to improve efficiency and effectiveness. 
- **Make difficult decisions:** They made a difficult decision despite facing resistance from their team. 
- **Overcome challenges:** They successfully navigated the challenges of the transition, including team resistance and operational hurdles. 
- **Focus on long-term value:** They prioritized long-term benefits for the team and the organization, even when it meant short-term disruption.
- **Learn from experience:** They identified areas for improvement in their communication and change management strategies.

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 22] - Building Diverse Teams

Tech has a massive diversity problem, and as a hiring manager, you shouldn't make that problem any worse (especially as diverse teams are scientifically proven to be better than homogeneous ones). From this lesson, you will:

- Recognize that common sourcing tactics like source-a-thons and friend-of-friend methods may introduce biases by yielding candidates similar to existing networks
- Learn strategies to actively promote diversity during the sourcing process, such as explicitly focusing on diversity in sourcing sessions to influence the diversity of the candidate pipeline
- Understand the importance of ensuring the top of the recruitment funnel is diverse, as this sets the stage for diverse hiring outcomes
- Discover ways to prioritize efforts in closing candidates, potentially focusing extra effort on diverse or outstanding candidates to ensure equitable recruitment practices

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

How Managers Hire Great Engineers [Part 10] - Friend-of-friend

Friendship is magic, especially when it comes to hiring great engineers. From this lesson, you will:

- Learn how to ask people for recommendations, without pre-filtering candidates, to identify strong potential hires
- Understand the importance of not pre-filtering based on market availability, as some candidates may be interested even if they aren’t actively looking
- Discover how using the recommender’s name in outreach can improve response rates

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

Q&A: Acing Tech Interviews In A Bad Job Market

Check out the full 4.5+ hour course diving even deeper into this session's concepts: [\[Course\] Ace Your Tech Interview And Get A Job As A Software Engineer](https://www.jointaro.com/course/ace-your-tech-interview-and-get-a-job-as-a-software-engineer/)

Get Taro Premium at a discount with our special link here: <https://www.jointaro.com/r/achiou15/>

As a Taro Premium community member, you can get personalized advice from proven tech leads like Alex and Rahul at any time by starting career discussions in our forum and joining [Group Office Hours](https://www.jointaro.com/event/group-office-hours-with-alex-career-direction-company-offer-evaluation-jan19/). 

This is the Q&A portion of the 1-hour session we did about finding interview success in a bad job market. You can watch that session here: [Acing Tech Interviews In A Bad Job Market](https://www.jointaro.com/lesson/Vz4jXfFIeQSSSzonpvH0/acing-tech-interviews-in-a-bad-job-market/)

Related resources for the questions in this video:

- [\[Masterclass\] How To Ace Your Big Tech Interview - Data Structures And Algorithms](https://www.jointaro.com/lesson/V4sTACzfOh22nMVcUV3w/masterclass-how-to-ace-your-big-tech-interview-data-structures-and-algorithms/)
- ["How can I get more internships?"](https://www.jointaro.com/question/k8TNvbobgU1R7ICDWKus/how-can-i-get-more-internships/)
- [\[Masterclass\] How To Choose A Good Company And Team As A Software Engineer](https://www.jointaro.com/lesson/0WEmTNZOMIh8xAdfO3JH/masterclass-how-to-choose-a-good-company-and-team-as-a-software-engineer/)

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.