You've probably seen those flashy YouTube videos talking about how easy it is to land an AI or ML job and get rich! They make it seem all you need is a few online courses. But let's be honest, it's not that simple. Getting into AI and ML is a lot harder than it looks. It's a field filled with complex concepts, endless learning, and frustration. Let's clear up the hype and get to the real deal.
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Let's take a look behind the curtain of an actual job listing. We have a Google AI Engineer position that might seem straightforward at first glance. But things get a little more complicated when we dig into the details.
The job description mentions you need nine years of CSS/HTML, nine years of Python, AND 7 years of Machine Learning experience. So, a front-end machine learning unicorn, huh?
It seems like they're looking for a mythical creature that can build beautiful websites, write complex algorithms, and train neural networks all at the same time.
Wait, What Level is This Really?
According to Levels.fyi (a website that tracks tech salaries), someone with 5-10 years of experience is usually a "Senior Software Engineer," while 10+ years typically translates to a "Staff" level at Google. If you’re not familiar with the leveling system, check out this video 👉 Mastering the Software Engineering Career Ladder.
But Google isn't using those titles in this job description, so who knows what level they’re actually looking for?
Decoding Tech Jargon:
It can feel like tech job descriptions are written in a whole other language, right? That's a common frustration!
I've been in the tech industry for almost 20 years. I was an early engineer at WhatsApp and even worked as a hiring manager at Facebook after its $19B acquisition. I am here to translate this code for you and help you understand what companies are looking for. By the end of this blog, you'll have practical strategies for breaking into AI, even without a fancy degree! Stay tuned!
To understand the competitive landscape in AI/ML and software engineering, I analyzed 80 job postings, including 32 AI/ML roles and 48 software engineering roles. These positions collectively attracted a staggering 85,333 applicants at the time of analysis. This data was obtained using LinkedIn's premium features.
There was a nearly 50:50 distribution for junior roles between Bachelor's (43%) and Master's (49%) degrees, with only 4% holding PhDs. Senior roles showed a similar pattern: 35% Bachelor's, 52% Master's, and 3% PhDs.
However, in AI/ML roles, the competition was even fiercer: 25% held Bachelor's degrees, while 52% had Master's and 11% had PhDs. This means breaking into AI/ML is as competitive as landing a senior role, at least in terms of education.
While a PhD might seem like a golden ticket to AI/ML jobs, it's often not a strict requirement. Many companies add a PhD to their job descriptions for several reasons:
However, a PhD is not always necessary for success in AI/ML. Many professionals have built successful careers without one.
The competition is fierce, especially if you're up against candidates with PhDs. If you can't land an AI/ML role right away, consider adjacent positions. Gaining experience in these roles can help you build a strong foundation and make a smoother transition into an AI career.
Why Focus on Adjacent Roles?
If you're fortunate enough to have multiple job offers, choose the role that excites you most. However, in a competitive market, take a strategic approach and don't be afraid to start in a related role. It can be your stepping stone to a fulfilling AI career.
While a job might not be your dream role, gaining experience is key. The sooner you start working, the faster you'll gain practical knowledge and skills highly valued in the AI industry.
Working in adjacent roles also allows you to work on more projects in ML/AI. This helps you build your portfolio and gain practical experience. Remember, the #1 thing companies look for when hiring is prior experience.
If interviews are scarce, go for less competitive roles. Highly specialized roles like AI/ML Architect, NLP Engineer, or Computer Vision Engineer often require advanced skills and experience.
More accessible entry points include Data Science, Data Analyst, or Business Analyst positions. These roles can provide you with a good understanding of data and problem-solving, which are essential for AI/ML.
Also, don't overlook Software Engineering. While it might not seem directly related to AI/ML, many software engineers successfully transition into these roles. The type of projects you work on can be a great stepping stone to an AI career.
While some roles might seem limiting, many data-related positions offer transferable skills that can lead to various other roles. However, QA roles might be an exception. They are often the first to be replaced by AI. If you're curious about which jobs are most likely to survive the AI revolution, check out this video 👉 Battle for the Future Work: Soon to be Extinct Jobs.
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