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.

Lack of Control

Engineers at Big Tech often experience a deep lack of control over their work, compensation, and long-term impact. Here are the core points from the lesson:

- Politics and top-down decision making mean engineers can invest heavily in projects only to have leadership reject ideas or directions with no recourse
- Compensation elements like company multipliers are tied to overall corporate performance, which individual engineers have virtually no influence over
- Constant reorgs, shifting org charts, and manager reshuffling create instability and make teams feel like they’re moving for reasons that aren’t always merit-based
- Big Tech frequently kills projects, even promising or widely used ones, because they don’t meet multibillion-dollar expectations, leaving engineers demoralized and powerless over product fate

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.

Just A Cog In The Machine

Although Big Tech offers compensation and prestige, the sheer scale of these organizations can lead engineers to feel like small cogs in an enormous machine. The work often becomes highly specialized and incremental, making it harder to find personal fulfillment or a sense of ownership.

- **Incremental Work Dominates**: Many engineers are tasked with refining already hyper-optimized features, such as tweaking a single call-to-action button on an ad format. This kind of work can feel repetitive and lacking in creative fulfillment.
- **Limited Ownership**: As teams grow larger, individual engineers may end up owning only a sliver of a product component—sometimes just one platform’s implementation of a single UI element.
- **Slower Innovation in Mature Products**: Legacy systems and products that have been around for over a decade (like Gmail) may offer fewer opportunities for impactful changes, making it harder to grow scope or gain recognition.
- **High Competition for Scope**: Even when surrounded by brilliant engineers, opportunities for meaningful ownership can be scarce. This leads to competition over small improvements and sometimes a struggle to stand out.
- **Pockets of Growth Still Exist**: Certain high-growth teams such as those working on newer initiatives like AI or experimental products can still offer dynamic, zero-to-one opportunities. Success often depends on finding and joining these pockets within the broader organization.

Poor Work-Life Balance

Working in Big Tech offers prestige and high pay, but it often comes at the cost of personal time and mental well-being. The demanding environment, peer pressure, and ambiguous policies can lead to overwork and burnout.

- **Competitive Culture Fuels Overwork**: The intense stack ranking and peer comparison systems push employees to work beyond normal hours to maintain high performance ratings and avoid being ranked at the bottom.
- **On-Call Stress and Burnout**: At scale, systems often encounter issues, and engineers may feel pressured to respond even during vacations, especially if they are domain experts.
- **Peer Pressure to Match Hours**: Engineers often feel compelled to match their teammates’ late-night work habits, contributing to a cycle of poor boundaries and collective burnout.
- **Unlimited PTO Backfires**: Policies like “unlimited vacation” often discourage time off, especially when company leadership sends contradictory messages about taking breaks.
- **Regional Differences Exist**: The culture of overwork is more intense in Silicon Valley compared to other parts of the world, such as Europe, where vacations are taken more seriously and work-life boundaries are better respected.

Peer Pressure

Working in Big Tech often means operating in a highly competitive environment filled with extremely talented peers. This creates both motivation and significant pressure, especially due to how performance systems are designed.

- Big Tech companies commonly use forms of stack ranking, where engineers are compared against peers and the bottom 5–15% may face probation (PIP) or termination each review cycle.
- Being surrounded by exceptionally strong coworkers can lead to intense pressure, imposter syndrome, and constant anxiety about staying out of the bottom cohort.
- High performers are often rewarded well, but lower performers may struggle, making the environment feel “dog-eat-dog.”
- Talent density can create problems with limited scope and many strong engineers competing for meaningful work, which can impact evaluations and career growth.
- Some teams have abundant scope and are great for growth, while others (like highly mature products) may have minimal impactful work, worsening peer pressure.

High Expectations

Expectations at Big Tech companies are extremely high and that pressure really shapes the engineer experience. Here are the core points from the lesson:

- Big Tech roles pay top-of-market compensation, and in return companies expect top-tier productivity, output, and code quality
- Many engineers coming from non–Big Tech backgrounds experience culture shock due to stricter standards, faster pace, and more rigorous release processes
- Performance reviews are a major source of stress, heavily peer-comparative, and central to promotions and job security
- Past eras of “rest and vest” are largely gone; in today’s leaner, more competitive tech environment, expectations - especially for newer and more junior engineers - are higher than ever

Well Roundedness

Working in Big Tech expands far beyond engineering; it shapes individuals into well-rounded professionals by pushing them to develop skills in communication, leadership, and cross-functional collaboration.

- **Development of cross-functional skills**: Navigating product launches or technical changes in a large organization requires coordination across many stakeholders, honing project management, communication, relationship-building, and system design abilities.
- **Environment fosters personal growth**: Operating within a large, complex structure forces individuals to adapt, influence, and lead through collaboration—skills less emphasized in smaller, more agile companies.
- **Preparation for an AI-driven future**: As coding becomes more automated, the remaining 80% of a top engineer’s job like communication, strategy, and leadership becomes even more valuable. This environment equips engineers for longevity in their careers.
- **Transferable experience for career switchers**: Even those new to tech can succeed by leveraging skills from prior careers (e.g., hospitality, education, operations). Communication and stakeholder management experience, in particular, translate well.
- **Resilience through versatility**: Focusing on foundational skills that transcend tech stacks or tools creates career durability, avoiding dependency on niche or outdated technologies.

You'll Learn Proper Engineering Skills

Working at Big Tech companies offers a unique opportunity to develop strong engineering foundations by being immersed in mature processes, robust tools, and a culture of high standards.

- **Access to top-tier engineers and tooling**: Employees are surrounded by highly skilled engineers and comprehensive internal tools that streamline development. Many of these tools address needs engineers didn’t even know they had until using them.
- **High-quality code review culture**: Rigorous code reviews from experienced peers serve as a fast-track for learning. Early feedback can be intense but leads to rapid growth in code quality, scalability awareness, and best practices.
- **Exposure to mature engineering systems**: Engineers learn the full lifecycle of software deployment with components like dogfooding, alpha/beta testing, stage rollouts, A/B testing, monitoring, and alerting, many of which are often missing or underdeveloped in smaller companies.
- **Longevity drives technical evolution**: Companies like Meta and Google have evolved with the industry, migrating through major tech stack changes over decades. This adaptability teaches engineers how to future-proof their systems and processes.
- **Cultural emphasis on continuous learning**: The engineering culture encourages innovation, resilience, and cross-pollination of ideas, making it ideal for those looking to grow as adaptable and forward-thinking software engineers.

Stability

Even amid recent waves of layoffs, Big Tech companies continue to offer more job stability than most others in the tech industry. This stability is driven by several structural advantages, not just current economic conditions.

- **Layoffs in Big Tech are more reactive, less  routine**: Unlike smaller companies or traditional enterprises that cut staff regularly every 1–2 years, Big Tech companies like Google and Facebook went over a decade without mass layoffs. Their recent cuts were responses to extreme macroeconomic shifts, not standard operating procedure.
- **Higher baseline of job security**: Statistically, it’s harder to lose a job at a Big Tech company, especially when compared to \~80–90% of other tech firms. That said, consistent performance is expected to maintain that security.
- **Stability comes from product-market fit**: These companies have already achieved PMF with multiple products—Google, Meta, Airbnb, etc.—which ensures reliable revenue and customer demand. This reduces existential risk compared to early-stage startups.
- **Strong financials provide a cushion**: Many Big Tech firms operate some of the most profitable business models in history, particularly in digital ads and cloud infrastructure. Their massive cash reserves and consistent profit margins further insulate them from financial instability.
- **Comparison with startups is stark**: Companies still seeking product-market fit or surviving on runway from funding rounds face constant pressure to cut costs, making layoffs more common and less predictable.

Future Job Prospects

Having a Big Tech name on your resume, whether Meta, Google, Uber, or others, often leads to significantly improved job-seeking outcomes. This reputation, deserved or not, dramatically influences how recruiters and hiring managers perceive candidates.

- **Stronger Resume Signal**: A stint at a Big Tech company increases perceived credibility and opens doors. Even a one- to two-year tenure can 100x the likelihood of landing interviews at other desirable companies.
- **Perception of Operational Excellence**: Recruiters and startup founders often assume that employees from companies like Amazon or Meta carry with them the systems, processes, and best practices that helped those firms succeed.
- **Proximity to Core Value Drives Perception**: Engineers who worked on flagship products or core systems (e.g., TikTok’s recommendation engine) are perceived as especially valuable, as if they embody the company’s strategic advantage.
- **Brand Halo Effect**: Just being associated with a known entity creates a trust shortcut—recruiters assume a baseline of competence and quality due to the rigorous hiring practices and peer environment of Big Tech.
- **Standardized Hiring Signal**: The structured interview process (e.g., Leetcode-style DSA questions) at these companies helps ensure a consistent bar, reinforcing the belief that Big Tech engineers are generally stronger than a randomly selected candidate.

Talent Density

Working at a Big Tech company means joining an environment that systematically attracts top-tier talent due to its high compensation and prestige. This creates a unique ecosystem where growth, learning, and problem-solving are significantly accelerated.

- **Talent Follows Compensation**: Competitive pay naturally draws in highly skilled engineers. While exceptions exist, the vast majority of engineers in Big Tech are above-average in both technical skills and motivation.
- **Noticeable Culture Shift**: Engineers often report that, compared to previous jobs, colleagues in Big Tech care more about code quality, engineering rigor, and continuous learning.
- **High Learning Potential**: Being surrounded by experienced and skilled engineers ensures a constant opportunity to learn. Teams often include individuals who are more experienced, creating a strong environment for mentorship and knowledge transfer.
- **Collective Wisdom and Problem Solving**: The scale and maturity of these organizations mean that most technical challenges have been encountered, and solved, before. Success often comes from leveraging internal networks to find engineers who’ve already navigated similar problems.
- **Standing on Shoulders of Giants**: Engineers thrive by observing and applying best practices from tech leads and staff engineers, accelerating their own effectiveness and impact through this ecosystem of shared expertise.

Competitive Pay

Big Tech companies are widely known for offering extremely competitive compensation packages, which is often one of the primary reasons engineers aim to work at these firms. This segment outlines both personal anecdotes and structural strategies that explain why compensation in Big Tech is so high.

- **Top-Tier Compensation Strategy**: Meta’s compensation goal, for example, is to pay in the 90th percentile—more than 90% of other tech companies—making total compensation significantly higher for engineers who make the transition into Big Tech.
- **Drastic Pay Increases**: It's common for engineers switching from smaller or non-FAANG companies to experience dramatic increases in pay - often as much as 50–100%.
- **High Cost of Living Justification**: These lucrative salaries help offset the cost of living in expensive tech hubs like Silicon Valley, New York City, and Seattle, where basic housing or property ownership can be out of reach without this level of income.
- **Prestige + Pay Combo**: Many engineers underestimate the financial upside of Big Tech and initially assume a 20–30% increase is generous. In reality, the combination of prestige and substantial pay is a powerful draw.

Big Tech companies are widely known for offering extremely competitive compensation packages, which is often one of the primary reasons engineers aim to work at these firms. This segment outlines both personal anecdotes and structural strategies that explain why compensation in Big Tech is so high.

- **Top-Tier Compensation Strategy**: Meta’s compensation goal, for example, is to pay in the 90th percentile—more than 90% of other tech companies—making total compensation significantly higher for engineers who make the transition into Big Tech.

- **Drastic Pay Increases**: It's common for engineers switching from smaller or non-FANG companies to experience dramatic increases in pay—often as much as 50–100%. Even the speaker was surprised by the magnitude of the raise upon joining Meta.

- **High Cost of Living Justification**: These lucrative salaries help offset the cost of living in expensive tech hubs like Silicon Valley, New York City, and Seattle, where basic housing or property ownership can be out of reach without this level of income.

- **Historical Offers Data**: In 2014, new grad offers ranged from $164K to $270K annually from Big Tech firms—Google, Facebook, and others. Salaries have only increased since then, due to ongoing talent wars and inflation.

- **Prestige + Pay Combo**: Many engineers underestimate the financial upside of Big Tech and initially assume a 20–30% increase is generous. In reality, the combination of prestige and substantial pay is a powerful draw.

Big Tech Is Not A Monolith

Although Big Tech is often discussed as if it's a uniform experience, that assumption doesn't hold up under scrutiny. The lesson clarifies that these companies, and even teams within them, can differ dramatically in culture, structure, and day-to-day work.

- **Big Tech is not a monolith**: Companies like Google, Amazon, Meta, Apple, and Microsoft may appear similar, but each has distinct norms and quirks. For example, Apple and Amazon differ significantly in operating style.
- **Team-level variation is substantial**: Within any large company - especially one with thousands of engineering teams—there’s huge internal variability. At Google, for instance, it’s unrealistic to expect all teams to operate the same way.
- **Meta as a case study in divergence**: Meta operates almost like four separate companies due to its major acquisitions (Instagram, WhatsApp, Oculus), each with its own leadership structure and internal culture.
- **Caveat for the audience**: The content that follows reflects common experiences shared by 70–80% of Big Tech engineers, but there will be outliers. Some parts may resonate; others may not - and that’s normal.

What Exactly Is Big Tech?

While many people associate "Big Tech" with the largest household names in the industry, the definition can, and should, be broader. This lesson provides a useful lens for identifying companies that operate like Big Tech, even if they’re not part of the traditional FAANG group.

- **Traditional Big Tech**: Commonly refers to the largest companies by market cap - *Microsoft, Google, Amazon, Apple, and Meta*. Sometimes Netflix is included (as in “FAANG”), but it's structurally different, e.g. Netflix historically had no engineering levels and treated everyone as senior engineers.
- **Critique of the FAANG Acronym**: Netflix is singled out as not entirely representative of "Big Tech" norms, being smaller in scale and structurally more unique than the others.
- **Broader Definition**: Companies with *$50B+ market cap* and *2,500+ engineers*, along with similar performance expectations, leveling systems, and engineering culture as the Big Five, should also be considered Big Tech. This includes companies like *Uber, Airbnb, and Stripe*.
- **Why This Matters**: Focusing only on the top 5–6 companies can be limiting. Many other companies now offer equivalent opportunities in terms of compensation, growth potential, and engineering culture, especially as they reach scale.

This broader perspective is helpful when evaluating job options and team culture - not all Big Tech companies are the usual suspects.

The Path To Career Success

Before diving into the pros and cons of Big Tech, we introduce two foundational career frameworks that offer different approaches to growth. These models help structure decision-making as one considers roles at large tech companies.

- **Explore vs. Exploit (David Epstein’s “Range”)**: In the early career stages, it’s valuable to gather a wide range of experiences—trying different roles or disciplines—to discover personal strengths and interests. This sampling period can lead to deeper, more informed commitments later.
- **Cultivate Passion through Skill (Cal Newport’s “So Good They Can’t Ignore You”)**: Rather than waiting to find a passion, pick a field and become excellent at it. Passion often emerges through mastery and impact in a chosen domain.
- Both frameworks offer valid paths and should be selected based on personal goals and life stage. The talk later connects these frameworks to the realities of working in Big Tech.

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).

Decoding the One Big Beautiful Bill Act for Tech Workers

New tax law, same tax code for tech workers… mostly.\
\
President Trump signed the One Big Beautiful Bill Act into law on July 4, 2025.\
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In terms of the impact to your taxes, this bill largely maintains the status quo by making permanent most of the tax changes introduced on a temporary basis through the Tax Cuts and Jobs Act in 2017.\
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It does, however, add a few new wrinkles to the tax code.\
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I’m hosting a live webinar next week to break it all down for tech professionals like you.\
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🎙️ Webinar: Decoding the One Big Beautiful Bill Act for Tech Workers\
🗓️ Date & Time: July 24, 2025 from 2-2:45 PM Pacific Time\
📍 Where: Live on Zoom\
💡 Especially relevant for:\
👉 Anyone in a high-income bracket that pays a lot in state & local tax\
👉 Folk planning to make Roth conversions in the next few years\
👉Those considering retiring at 65+ in the next few years\
👉 Anyone considering taking a career break or on an ACA plan\
\
We’ll cover:\
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✅ Changes to the deductibility of state and local taxes\
✅ How the new bill affects ISO exercises and your likelihood of paying AMT\
✅ What's up with Section 174?\
✅ Changes to QSBS\
✅ "No tax on tips/overtime"\
✅ Deductibility of new U.S. car loan interest\
✅ The new "Trump account"\
✅ Impacts to Medicaid\
✅ The new bonus deduction for seniors\
✅ Updates to clean energy and EV credits\
✅ How the new rules impact ACA health insurance subsidies

Machine Learning System Design Ask & Learn

**Event description**

Join us remotely for an interactive Q&A with Ilya Reznik—Founder of MLEPath, veteran ML engineer with nearly 15 years at Meta, Twitter, and Adobe, and interviewer of hundreds of real-world ML system design (MLSD) interviews (plus nearly 1,000 mock sessions in the last two years). Whether you’re tackling your first MLSD interview or leveling up for senior roles.

- **Core frameworks & approaches**: how to structure your end-to-end design, balance scalability, latency, cost, and reliability
- **Key components & trade-offs**: data pipelines, model serving, feature stores, monitoring, and A/B testing
- **Real-world insights**: pitfalls Ilya’s seen candidates fall into—and what top performers do differently
- **Live feedback**: get tailored advice on designing everything from recommendation systems to real-time anomaly detection

Perfect for ML engineers aiming to land roles at FAANG, startups, or anywhere that demands rock-solid system design chops. Come ready with your toughest questions—and leave with a clear, actionable roadmap for your next MLSD interview.

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.

Where's The Scope?

Lack of Control

Politics

Just A Cog In The Machine

Poor Work-Life Balance

Peer Pressure

High Expectations

Well Roundedness

You'll Learn Proper Engineering Skills

Stability

Future Job Prospects

Talent Density

Competitive Pay

Competitive Pay

Big Tech Is Not A Monolith

What Exactly Is Big Tech?

The Path To Career Success

The AI Frontier: Get Paid To Build Smarter LLMs

Decoding the One Big Beautiful Bill Act for Tech Workers

Machine Learning System Design Ask & Learn