There is a gravitational pull in software engineering that is hard to overstate. From the moment most developers write their first serious line of code, the implicit career narrative is already forming: get good enough to pass the interview, land the job at Google or Meta or Amazon, and you will have made it. FAANG — or MANGA, or MAANG, or whatever the current acronym is — functions as the field's definition of success. The salary data is real, the prestige is real, and the résumé signal lasts a career. I am not going to pretend otherwise. But the best developers I have worked with, learned from, and genuinely respected over the years were almost never optimizing for that destination — and I think the reason why tells you something important about what actually makes a great engineer.
This is not a post about whether FAANG jobs are good or bad. Some of the sharpest engineers alive work there and grow enormously because of it. This is about the specific habit of chasing FAANG — of structuring your learning, your projects, your career choices, and your sense of professional self-worth around the goal of landing there. That habit, in my experience, produces a particular kind of engineer. And it is rarely the kind I want to work with.
What FAANG Chasing Actually Optimizes For
To understand the problem, you have to look honestly at what the FAANG interview process selects for. It is a well-documented, highly specific filter. It rewards:
- Memorized solutions to a narrow category of algorithmic problems
- Performance under artificial time pressure on decontextualized puzzles
- The ability to talk through structured problem-solving in a scripted format
- Consistency across multiple rounds of the same type of evaluation
None of those things are useless. Algorithmic thinking matters. Communication under pressure matters. But the skills the FAANG interview process selects for are a thin slice of what makes someone an excellent working engineer — and the preparation required to pass those interviews is intensive enough that it crowds out almost everything else.
An engineer who has spent six months doing LeetCode problems daily to prepare for a Google loop has spent six months not shipping real products, not debugging production systems, not reading other people's codebases, not making architectural decisions with real consequences. The interview prep is optimizing for the interview. It is not optimizing for engineering.
The Steel-Man: FAANG Chasing Has Real Upside
Let me take the other side seriously, because it deserves it.
The compensation argument is not trivial. Total compensation packages at top-tier big tech companies are genuinely life-changing for many engineers, particularly those without generational wealth or expensive degrees from well-networked universities. Optimizing for FAANG is, for some people, a rational financial decision that buys freedom — the ability to take risks later, to work on passion projects, to leave a bad situation without financial panic. Dismissing that as shallow misses the real stakes for real people.
There is also a genuine learning argument. The scale at which FAANG companies operate produces engineering problems that simply do not exist elsewhere. If you want to understand distributed systems at the level where milliseconds of latency affect millions of users, or machine learning pipelines at a scale where model decisions have societal implications, there are very few places outside big tech where that exposure is available. For engineers drawn to those specific problem spaces, FAANG is not just prestigious — it is the right environment.
And the résumé signal is real in ways that have downstream career value beyond the job itself. A Google or Meta stint opens doors for decades, including to the kinds of early-stage startups and technical leadership roles that the FAANG-chasing critics often valorize. The credential is not nothing.
I hold all of that as true. And I still think the engineers who build the deepest craft are almost never the ones who made FAANG the organizing principle of their career.
What the Best Developers Optimize For Instead
The developers I have learned the most from share a recognizable pattern. They are obsessively interested in specific problems — not in career outcomes. They gravitate toward work where they have genuine ownership and can see the direct consequences of their decisions. They read widely, across domains and codebases and industries. They care deeply about the people using what they build. And they measure their growth by how their thinking has changed, not by what their current employer's logo looks like on LinkedIn.
That orientation produces a different kind of engineer. One who:
- Has built real intuition for production systems because they have owned them end to end, including the three AM pages
- Can navigate ambiguity because they have regularly worked in environments where nobody handed them a well-specified ticket
- Has genuine opinions about architecture because they have lived with the consequences of their own past decisions long enough to be humbled by them
- Knows how to work with non-engineers — designers, product managers, customers — because they have had to, repeatedly, without the insulation of a large organization
- Has shipped things that failed, learned why, and built the next thing better
That profile is not something the FAANG interview process filters for. It is also not something that FAANG chasing tends to produce, because the habits it requires — obsessive interview prep, credential optimization, résumé gap anxiety — are almost the opposite of the habits that build deep craft.
The Credential Trap Is a Thinking Trap
Here is what worries me most about FAANG as a career organizing principle. It is a framework that locates your professional value externally — in whether a particular set of companies have validated you. And frameworks that locate value externally tend to persist as habits of mind long after the specific goal is achieved or abandoned.
Engineers who spent years chasing FAANG and got there often find a new external validation target — the right internal team, the right promotion level, the right performance rating. Engineers who spent years chasing FAANG and did not get there often carry a persistent sense of inadequacy that has nothing to do with their actual capability. Neither outcome is a healthy relationship with craft.
The engineers who compound the most over a career are the ones who internalized early that the feedback signal that matters is the work itself — whether the thing you built actually solves the problem, whether the system you designed held up, whether the team you worked with got better or worse because of how you showed up. Those feedback signals are available at almost any company. They are not exclusive to big tech. And they are more honest than any interview loop.
Where I Land
If FAANG is genuinely where your specific interests and problems live — go. If the compensation would buy you a kind of freedom that matters for your life — go and be clear-eyed about why. There is no shame in either of those reasons, and the work done at those companies is often legitimately impressive.
But if you are chasing FAANG because it is the default definition of success that the industry handed you before you thought carefully about what you actually want to build — that is worth examining. The engineers who shaped how I think about this craft were not optimizing for a logo. They were optimizing for understanding, for ownership, for the satisfaction of building something real and watching it work. That is a slower path to a certain kind of prestige. It is a faster path to actually getting good.
And getting good, it turns out, opens more doors than any interview prep ever will.
