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What is the best part of working as a Machine Learning Engineer?

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Shruti’s Answer

With ML the possibilities are limitless.
If you are ready to work hard then you will end up working in awesome companies, deliver some amazing tech that can help the world and also earn a fantastic sum of money.
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Rodrigo’s Answer

Hi Jiarong J.,

You can work in many technology companies or any other kind of great companies, working in AI projects, programming inteligent apps or intelligent devices, you will never stop learning something new, machine learning can help people to improve their lives and sometimes making predictions of information that can help a company to sell more, develop new technologies or entering new markets.
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Vikas’s Answer

Its the perfect role for someone who wants to drive impact through cool new features for products. Customers receive innovative ideas really well as a lot of them have been bought into the potential that AI has to improve the lives of folks in general. I would say its also the best role for someone who loves coding and also has a love for math. Very few other type of programming roles effectively combine both coding and stats skills so well. Of course the market is always hot for machine learning engineering professionals due to the scarcity of supply relative to demand. This creates a sweet spot for being able to work at multiple companies and massive pay raises when moving from one role to another if that motivates you.

Best of luck
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Dhairya’s Answer

I am an AI researcher that also does some ML engineering for a fintech startup. My work sits at the intersection of cutting edge research and product development, where I work to solve hard NLP problems for our conversational AI products. For me the best part is being able on r challenging and creative problems. No two days are ever the same for me. My work ranges from reading research papers, building proof of concepts to deploying new deep learning and ML solutions. Compared to academia, work in industry is hard as the problems are real world based (not small toy datasets), the data is messy and often non-existent and the problems require creative thinking to solve. The other additional perk is you get to work with really smart and diverse people. People in ML and AI come from diverse background and it's a lot of fun to learn more about everyone's diverse interests.
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