This essay is based on a conversation with Manoji Tum, a 23-year-old machine learning engineer from Meta, based in Menlo Park. Edited for length and clarity. Business Insider has confirmed Tumu's salary and employment.
Machine learning has become mainstream.
I started my Masters program in 2022. Around the time ChatGpt was released. With advances in AI and AI tools, it has become a highly competitive field, with many people looking to participate.
I had college credits from the classes I took in high school, so it took me a year to get my bachelor's degree. He then worked full-time as an engineer while earning his master's degree in AI. After my master's degree, I played a role at Amazon. I worked there for nine months as a machine learning software engineer.
While I was on Amazon, I saw Meta do lots of cool machine learning. When an interesting role came out, I applied to their website or LinkedIn. I learned a lot on Amazon, but I thought there was a more interesting work going on in Meta.
In June I left Amazon and joined Meta as a machine learning software engineer for a total reward of over $400,000. I was really excited about it and wanted to get a job as soon as I received the offer. Here's my advice to try to break into this field.
Understand machine learning and its changes
One thing about the role of machine learning is that there are many variations in the title. Some companies may be research scientists, applied scientists, software engineers, or machine learning engineers. At Meta, I am a machine learning software engineer for the advertising research team.
My role is to combine research and implementation, but more research. With all the advancements in AI, many papers on AI and machine learning can be read. It focuses on staying on the cutting edge of Meta using the latest research and models.
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Machine learning has shifted very much. Previously, using classical techniques was much more embraced. This relied on humans to make decisions about data representation. Currently, the focus is on deep learning. It utilizes artificial neural networks to automatically learn functionality from raw data.
People really understand how powerful it is, and that has led to non-technical companies investing in machine learning systems.
Try taking a high-tech internship while you're at university
Most of my applications for large tech companies are website cold applications. I was not referred to my role on Amazon or Meta. However, I had a decent resume and found it very easy to get an interview.
Experience is the biggest factor. Try to get all kinds of internships at university. I think they stand out the most. When you look at your resumes where people post online and seek advice, you see projects and programming languages taking up space.
Emphasise your experience around high-tech resume projects
It's good to highlight the projects you've worked on, but I think they're putting too much emphasis on their resume. They are useful to include, but they should be more supplementary. By the time I first started applying for Amazon and Meta roles, I had already removed the project from my resume.
My general advice is that if you have two or three years of experience, it's okay to remove the project and focus more on highlighting your experience.
Big Tech has a very standardized interview process – avoid one common mistake
Meta recruiters emailed me and said they came across my profile but never told me how. Immediately I called and reapplied.
This process was similar to when I interviewed at Amazon:AA First Screening. Then I went through 4-6 rounds of interviews and asked questions about coding, machine learning and behavior. It took about a month and a half and was one of the smoothest interview processes I had. I was very pleased with everything.
One of the mistakes that people think make is to make it a wing during an interview with the action. Thankfully I didn't. I studied the value of the company to prepare. I had a huge document that had the stories written down to answer each question and follow-up I would have.
For example, Amazon has these leadership principles, where we coordinated those online and prepared stories when we interviewed them, looking into the prepared stories. There are a few things you can study in Meta, which I have posted on its website and I have adjusted my story in particular to them.
Don't worry about pay when you first enter machine learning
I made the mistake of not doing an internship while I was in college. However, I was able to secure a contract role right after my undergraduate student. This was the beginning of 2022 and it would have been possible to acquire a software engineering role without much experience.
Apply for any internship, any internship. When I was choosing machine learning and software engineering roles for my first job, I decided that I wasn't very interested in the traditional software engineer roles.
Before I started working on Amazon, I chose the low-wage machine learning role.
Are there any AI career stories to share? Please contact this reporter, Agnes Applegate aapplegate@businessinsider.com.
