he works as a machine learning engineer at a Big Tech company.
On paper, I had a dream job:
- Flexible operation
- Smart and friendly colleagues
- Great benefits and advantages
- Good work life balance
- Few meetings
- And my compensation was over $100k
Despite all this, I always felt that something was missing.
At first I thought it was a phase and I needed to give it more time, but the feeling didn't seem to go away as the months went by.
If anything, it intensified, and I began to feel less motivated.
I love this industry so much; I've been blogging and recording YouTube videos about data science and machine learning for over 3 years, but this past year, I haven't felt the same excitement.
This made me very sad, as I am just beginning my journey, and there are many things left to learn.
I knew something had to change.
I wanted to find that love and excitement that I had only two years ago.
So, in this article, I want to look at why I finally quit my machine learning engineering job, and give a different perspective on what these “dream” jobs are really like.
Needless to say, this is only my opinion from my short experience in one group and should not be taken as a reflection of the company or its people.
Speed
Even though Big Tech is clearly technology companies, that doesn't mean they move that fast when it comes to testing and replicating ideas.
As companies grow, they naturally hire more employees and add more layers to their business structure. Later on, the bureaucracy slowly crept in.
There is not much you can do to avoid it.
This happens when the company is generally doing very well and making a lot of profit.
As the old saying goes:
If it ain't broke, don't fix it
Therefore, these companies are less likely to explore new ideas or strategies to protect their bottom line.
They are less willing to make big, risky changes, so to speak.
I get it, it makes perfect sense.
However, for people like me, this kind of culture is not suitable for me.
Truth be told, I am a very intelligent, pragmatic and action oriented person.
I don't bother to check all the intricate details, or spend a lot of time completely randomly “What if” questions and going down the analysis-disability rabbit hole.
The best strategy, in my opinion, is to have 80% confidence in your idea that you will work with offline testing, worst-case modeling, etc., and then send it to production to see what happens.
Some people may think that is reckless and somewhat stupid.
Okay, I've learned that you can't please everyone.
For me, this method is very exciting and inspiring as you always get to see your creations come out into the world.
Sure, sometimes you'll pull out completely, but that's the point of the process.
It's iterative, and you learn and build a better product next time.
Unfortunately, this way of working is not compatible with the culture of large companies, or at least not compatible with certain groups, in my experience.
In short, it didn't fit with the way I worked, so I struggled to stay motivated.
Lack of Purpose
It's a cliché to say that you're just a small person in a big machine, but that's exactly how I felt.
A few months later, I realized that my job didn't matter that much.
Sure, it made an impact, but in the grand scheme of things, it was just a drop in the ocean.
Regardless of whether I was there or not, the company was operating well, making a profit and continuing to rake in income for shareholders.
Don't get me wrong, I understand that it is a perfect example of good business and how a company should be run.
However, it made me feel useless and purposeless. Whatever I was doing was in vain, and that really affected me.
This probably comes from some pride, but I wanted to feel really important and ultimately in charge of where the company was going.
When I leave the company, I want them to feel it.
Being useful is what brings me purpose, and I finally didn't feel that during the last year.
Internal Tools
This is a small one, but many of these large companies have a number of internal tools that they have developed over the years to increase productivity.
For example, instead of working with AWS directly, the company has its infrastructure engineers build wrappers around AWS to make its core services easier to use and better manage role permissions.
Google is one company that is known for having a lot of internal tools, but most sources say they are pretty good.
While this sounds good on paper, you don't learn how to use things like AWS properly, so you don't gain transferable skills that you can use in other roles if you decide to leave.
In my experience, there were many internal tools for the basic skills I wanted to learn:
- Using cloud systems
- Building an infrastructure for model distribution
- Default setup on Git/GitHub
These were just handed to you on a plate, and I didn't have to think twice about it.
Sure, it improves productivity, I'll give you that.
But I'm someone who wants to really understand what's going on under the hood all the time, because if something breaks, I want to know how to fix it.
I didn't feel like I learned much from this, and it's not what I'm looking for at this point in my career.
Small Scope
There were about 100 machine learning engineers across the company, and about 5 times that number across the data, machine learning and science organization.
Given this number of employees, many of the products and algorithms were so mature, that it was very difficult to extract any additional benefits or make a significant impact.
It's not really a bad thing, and it's obviously my job to find ways to improve.
That's what I was paid to do.
However, when you have hundreds of people who are working or have been working on the same algorithm for more than a decade, the scope of improvement you can make is very small.
The only real way is to redefine how to approach the problem. But, as I said earlier, no established, profitable company is going to want to spend a year redesigning an entire system.
It just doesn't work, and it's not worth it in the eyes of senior leadership.
Most of the work I did was maintenance and maintenance.
There was little to no new features or algorithms, and over time, the work became old and uninspiring, as I said at the beginning.
What's Next?
The easy route was to stay, eventually get promoted to senior machine learning engineer, and have a comfortable, well-paying job for the next decade.
But where's the fun in that?
I'm only 26 years old, and if there's one thing I've learned about myself in the past year, it's that I'm more of a risk taker and more of an entrepreneur than I first thought.
I want to build great things that no one else has, and make my own little dent in the world.
Many people will roll their eyes or laugh at me when I say that, which they have done before in front of me.
But that's the price you pay when you're delusional and want things that others are too afraid to try or say.
So, I decided to do a complete 180. I'm going from Big Tech to being the sixth hire at the start.
Big change, high risk. But as the saying goes:
Nothing changes, if nothing changes.
I'm very excited about this new project, and I can't wait to help build the unicorn.
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