angarg12

Not an answer, just wanted to say that regardless of the outcome you are doing well. A few years back I was doing 70k GBP in Cambridge (expensive as fuck) in my mid 30s. I could only imagine pulling +100k in a LCOL in my 20s.

Congrats.

> but that distinction has never been relevant to my work

This is a very junior take, "I have never used it myself, so it isn't important".

As an API provider, it is quite important for me to see if my app is spitting out tons of errors 401 or 403.

As the saying goes, people quit managers, not companies.

That sounds odd. With that much experience you should at least be getting offers in the 200k range easily. Are you cold applying or using referrals? I found these days CVs get screened by software that's very sensitive to keywords, and it's very easy to get auto-rejected.

I documented my process of getting job offers here, you can check it out in case it helps

https://www.teamblind.com/post/My-experience-getting-offers-...

I just wrapped up a long job search process, where I got offers from a handful of companies. I currently work at FAANG, so I was looking for a pay bump.

The only companies that could beat my current comp are other big tech and finance, but there were a couple of surprises along the way.

For example, a Tier 3 (i.e. you never heard about them) company offered me 450k TC for a Staff level position. This compares to the 500k TC for a senior level position at FAANG. I also interviewed for a Startup that offered 650k TC for a Principal level, although the stocks were paper money.

To your point, "random" companies can come close to FAANG salaries for high enough levels, but it's far from a given. In fact I'd say most random companies probably top in the 200k range.

If you are interested just start every conversation with a recruiter asking for their comp range. I found these days almost all recruiters answer, and then you can decide if it is worth your time to go ahead.

I'm not so sure. I come from southern Europe where doing something like that is easy, because people mean it. But living abroad I can probably count with one hand the times that someone made good on that. Sometimes I followed up with someone, just to get excuses and vague answers. I had to learn the hard and awkward way that most people are just trying to be polite and don't really want to "stay in touch".

Doing it seriously takes real work.

> Startups ship more per person than big companies – everyone knows this. But how do you retain that advantage as you scale? Our answer is small teams

> Right now we're 47 people

I'm sorry, but you haven't solved scaling small teams.

Queues are a tool in your distributed system toolbox. When it's suitable it works wonderfully (typical caveats apply).

If your perception is indeed correct it'd attribute it to your 3rd point. People usually write blogposts about new shiny stuff.

I personally use queues in my design all the time, particularly to transfer data between different systems with higher decoupling. The only pain I have ever experienced was when an upstream system backfilled 7 days of data, which clogged our queues with old requests. Running normally it would have taken over 100 hours to process all the data, while massively increasing the latency of fresh data. The solution was to manually purge the queue, and manually backfill the most recent missing data.

Even if you need to be careful around unbound queue sizes I still believe they are a great tool.

If you have two workloads with very different operational profiles (e.g. low latency low volume vs high latency high volume) you could also process each workload in a different copy of the system, sorta-kinda the hot-path/cold-path pattern.

It might sound like duplicating your system would make it more complex, but having two systems each doing one very specific thing tends to be simpler than one single system doing very different things.

Code quality and how often it is read are mostly orthogonal.

You can have an stinky old piece of legacy software that no one ever reads because that system is deprecated and slowly getting phased out, and we can live with it in the meanwhile.

You can have the most elegant a beautiful piece of code that gets read a lot, because it is in the critical path of a very active and rapidly evolving system, and people need to reference, modify and extend it a lot.

However there is a crucial way where OP is wrong: there is a pull and push between code quality and how often it is read. If your code is a legacy big ball of mud, people will be less inclined to touch it, probably going to the extend of rewriting the whole thing. On the other hand when code is well written, people are less afraid of digging into it.

Lastly, even if OP thesis was correct, I don't know think it would change anything. Strive to write the best code you can within reasonable constraints, independently of how much other will read it (which in my experience is almost always underestimated).

My job title is ML Engineer, but my day to day job is almost pure software engineering.

I build the systems to support ML systems in production. As others have mentioned, this includes mostly data transformation, model training, and model serving.

Our job is also to support scientists to do their job, either by building tools or modifying existing systems.

However, looking outside, I think my company is an outlier. It seems in the industry the expectations for a ML Engineer are more aligned to what a data/applied scientist does (e.g. building and testing models). That introduces a lot of ambiguity into the expectations for each role in each company.

Previous years I would have completely agreed with this post. Nowadays with the AI and ML craze I'm not so sure. I've seen plenty of companies using using vast amounts of data to train ML models to incorporate to their products. Definitely more data that can be handle by a traditional DB, and well into Big Data territory.

This isn't a value judgement about whether that's a good idea, just an observation from talking with many tech companies doing ML. This definitely feels like a bubble that will burst in due time, but for now ML is turbocharging Big Data.

> Here’s a little-known secret: most six-nines reliability software projects are developed using a waterfall methodology.

I've designed and deployed Tier 1 services for a Big Tech company, and here's is a little-known secret: when nothing changes, our reliability is higher than six-nines.

Last year I measured our uptime during Black Friday for fun. Our error rate was measured in scientific notation because the number was so small. We didn't do any deployments or changes during that period.

When you operate in a steady state it's easy to achieve zero errors, and most downtime comes from random failures in hardware, i.e. servers crashing or network blips (which, operating at scale, are relatively common).

So my and other's personal experience is that most outages are due to changes in the software, dependency outages, or the rare large scale event that completely kills your SLA (e.g. a whole AWS region is down). Taming these is at the essence of reliable software.

Whoever tells you that the best software is made using waterwall methodologies from a fixed and never changing set of specifications, lives in a fantasyland alien to the vast majority of developers.

What the hell is going on with this article?

The article posits that NIMBY-ism is a myth, and to prove it, they do a thought experiment that shows that building more density in Palo Alto would yield more value, and hence it would be the rational thing to do. But why use an unrealistic thought experiment when you have real world examples of NIMBY-ism in action?

This article makes no sense at all.

Sounds a bit delusional to me that you can just "teach someone data structures in 1 hour".

Also the role specifics matter here. It might be ok to bring in a junior who needs lots of mentoring onboard. For a tech lead who is going to be driving the direction of a team, I'd expect them to be up to speed and autonomous very quickly.

I'm the same age than OP, but spent most of my adult life living paycheck to paycheck. I have only obtained a comfortable income and bumped up the class ladder during the last couple of years, when I finally moved to the US working for Big Tech.

I don't know what is the point of this article other than a humblebrag. What should the takeaway for people struggling to make ends meet while working bad, low paid jobs be? Should I feel bad that I'll never get to retire before 40 because I didn't ride the high income wave since my 20s?

This is pretty much the main reason why I will move from US to Europe in the next couple of years. I'm not keen on dumping my life savings into a down payment and then have a balloon mortgage to buy a shack in my area when I could use a similar amount of money to basically buy myself a palace back there.

This was an internal tool, so no use in fluffy marketing.

I think this was a folly by our director. About a year after I left I learnt that he had "left to pursue other opportunities", which is corporate speak for "he got canned".

Working for big tech, our Director wanted to add "ML" to one of our workflows no matter what. He identified a "problematic step" and a team of scientist spent 1 year coming up with a ML model to replace this step. I was asked to deploy this solution in production.

When we tested the model (which somehow hadn't happened before) we found that the workflow was 5% quicker, but the results were more than 10% worse, and was more expensive to run. Everyone hated the idea, but the director kept pushing to do it, "or I'll find someone else to do it". I ran away and never looked back.

All of the downsides are 10x worse when doing infra manually.

Just do IaC well to avoid the pitfalls, rather than polishing the turd of manual infra.

US cities are remarkably different than European cities. I personally consider the likes of Seattle, SF or San Jose a grade A shithole. NY felt a bit better, but I can see why people consider it dehumanizing if you live in Manhattan.

If you have never been abroad, I'd recommend you take a long trip to a European city and try to live like a local. If possible I'd avoid the world class metropolis that might feel a bit less homely, and target for smaller cities instead. My aim is not to change your mind about suburbs and cities, instead the experience might expand your mind and help you see why people love the lifestyle.

Where exactly did you live? My experience is the polar opposite.

I'm visiting my native city in souther Europe after 2+ years living in the US suburbs (Seattle). When I go outside, there is a plaza full of children playing (I can hear them from my window in a 5th floor), people sitting in a bench chatting, and terraces full of patrons. In my suburbs in Seattle I never see children "going to school", I just see a School bus that stops and has some kids jumping inside. I also never see anyone around when I walk the dog, except that faithful old man going for his morning jog.

Also how is having the ability to drive everywhere more humanizing? here I just don't have to. Yesterday I had 15 minutes until a meeting, so I just went downstairs, crossed the street to pop by the supermarket, bought something and had time to spare to join the meeting. In the US it would take me that much just to drive to the closest strip mall.

I inherited thousands of untyped Python code for critical systems, and it was an unmaintainable hot mess that caused a constant stream of pain. No kidding that was one of the main reasons we had to build a second version of the system.

I love Python, but I use it carefully in large scale production settings. Python with a Typescript-like static type system would hit the sweet spot for me (and yes, I've used mypy, but it doesn't hit the same spot).

Besides all the points in the article, there is one more point that infuriates me about RTO mandates, and it's the double standards.

Director level and above at my company have much more flexibility to WFH that regular employees. In fact several of them in my org do it.

During an all hands, someone asked one of them about the RTO mandate, since they worked remotely. The director answered with a straight face that although he works remotely, he built the network that got him promoted to Director going to the office, and that's why you should go. If that level of patronizing doesn't make you lose faith in leadership, I don't know what does.

> You got to enjoy the high but how you have to survive the lows.

But I have been in the same employer for the last 6 years, so I didn't get to enjoy the high :/

I wrote a reply to a person who was having a similar problem in another forum. I'll copy my post here.

I am a ML Engineer working at a big Co with 15+ YoE. This is how things are going for me:

* I started looking in September last year. So far I had 3 complete loops. One rejected me and two down leveled me.

* Although recruiters reach out a lot, more than half of them ghost me.

* When I started looking I used to ask for salary bands. Most companies top of band was around half of my current comp, so I declined. Now I'm saying yes to just about anything, just to prove to myself that I can get a job offer.

* Particularly in the ML space, requirements from companies are simply insane. One company was hiring a role doing exactly what I'm doing in the same domain. They rejected me because they wanted someone who was an expert at all of software engineering, data science and this particular domain, and apparently I'm not good enough. Oh, there is also this company who rejected me at the screening phase because I had "only" 6 years of experience with their particular technology instead of 8.

Draw your own conclusions about how good or bad the job market is.

The wording here is a bit odd, almost like blaming people for delaying childbearing. The world is complicated and a number of factors have produced this outcome in the developed world.

My parents were factory workers and they encouraged me to study a university degree as a sure way to a successful career. I finished my degree well into my 20s, but then the economic collapse of 2008 happened and I spent several years living paycheck to paycheck, lucky me who at least had a job.

In my late 20s I finally broke from economic stagnation by moving abroad. Then I spent the next 12 years moving countries every 2-3 years, which isn't good for stability. In fact I didn't meet who would become my wife until my mid 30s.

Now I approach 40 and have a good paying job in tech. However I'm in the US on a non-immigrant visa and my company has done waves of layoffs that I luckily survived. We are seriously considering having a child, but the prospects don't look great. Everything else aside, we don't really know anyone or have a support network here.

I know most of this is moaning and if we "really wanted" we could make it work. But it doesn't discount the fact that it's easier to start a family for someone with a stable job with a support network.

I've also conducted hundreds of interviews at Big Tech and my experience is far from OPs. I can probably count with both hands the number of times that someone was technically weak across the board, but they got an offer due to their attitude and soft skills. All of these occasions were for intern or junior roles.

I agree that companies overindex on pretense of technical competence (e.g. asking leetcode hard questions), but I'd be very skeptic of extending an offer for a software engineer role who has literally failed all tech rounds, much more for Senior+ roles.

I've also worked with people like Chen, upbeat, likable people who just don't seem to get anything done. Because they are so likeable they get away with it for a long time, because who can be mad at them? However this almost always turns into a net negative for the team.

Going back to OP, who is the title aimed at? if it is a wakeup call for companies and hiring managers to obsess less on obscure coding puzzles and value soft skills and experience more, I'm all for it. If it is advice for job seekers in the current market, I'd say you should still focus on tech skills.