Machine Learning At Scale

Machine Learning At Scale

What would I do if I wanted to get into ML in 2026

Ludovico Bessi's avatar
Ludovico Bessi
Jan 28, 2026
∙ Paid

Introduction

I have been fortunate enough to graduate in late 2021 when hiring for new grads was going strong and ML skillset was somewhat rare.

In that time, I got offers from:

  • Google Zürich (still here rocking almost 4 years after!)

  • Meta London

  • Snapchat Vienna

  • Yelp London

  • UBS Zürich

All for heavy Machine Learning.

But, we know how many openings there were around those times…

Funny thing is that I know for sure I bombed hard one google interview, but still got in on the back of four strong interviews. Not sure that’d have happened in late 2025 early 2026 market?

Having said that, I remind myself that lucks meets opportunity, but you can't control luck. So all that’s left is creating the opportunities for yourself!

Let’s see what I’d do in 2026 to stand out, in two different cases:

  • Students

  • Early career professionals

Let’s go!

Let me say the quite part out loud: you should think of answering the following:

“How makes me different from other students in my cohort?”

This is not to be taken in a zero sum game aka competition, but that’s literally what companies are trying to answer when deciding to select you over others.

With that guiding light in mind, let’s go!

Students

If I was a student, I’d stop optimizing for one dimension that every students optimizes for: GPA

Early undergrad:

I’d get involved as early as possible into student clubs and other opportunities to start padding your CV.

I’d immediately start applying to early career opportunities, as an example: STEP career program from Google.

I’d also get into opportunities where my effort is directly linked to output:

  • Preparing for a Google Summer of Code (GSOC) application: the more you contribute the higher the chances of getting in

  • Contributing to open source code. Imagine being top PyTorch contributor. I assure you will get fast tracked to Meta hiring. (plus, the skills you gain are incredible!)

Later undegrad:

Here, I’d capitalize on the work you did earlier to start winning some nice internships. Prioritize work experience and not courses, you should try and fight against your uni to be able to follow as much as possible Co-op model of UWaterloo. (go look it up, tldr is that you do 6 months internship / 6 months courses on repeat).

You did not do anything in your early undergrad? No problem! I started in my third year of undergrad getting involved in work opportunities. Just go read section above again ;).

At this point, you should have an impressive CV even before graduating and should be able to get tons of interviews. Interviews are also a game of luck: you win some and you lose some, but you just need to win once. So get it!

Early career professionals that want to break into ML

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