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Best Machine Learning Companies to Work for in 2018

According to IDC, the spending on Machine Learning and AI is anticipated to grow rapidly from less than $8 billion in 2016 to $47 billion by 2020.IDC predicts that 40% of digital transformation initiatives will be supported by AI and Machine Learning by end of 2019. For the past year or more the term ‘Machine Learning’ has been sprawling like wildfire, and as a consequence, companies are investing in Machine Learning and hiring machine learning experts to support it. Every major tech company is eager to hop on board of digitization and is implementing or has plans to implement machine learning to build innovative machine learning products. Whether it is Apple’s Siri or Google’s self-driving cars or Pinterest serving relevant pins to every user or it is Facebook delivering relevant ads to users based on the pages they like – these are some of the top machine learning companies that have built fantastic machine learning products to get people excited and engaged. Here is a consolidated list of the best machine learning companies that make your best bets if you want to pursue a machine learning career –

Please note: This list of best machine learning companies is NOT a ranking. So company number 3, for instance, is not a “better” machine learning employer than company number 5.

Best Machine Learning Companies to Work for in 2018

Top Machine Learning Companies to Work for

1) Amazon – Ranked #1 among the Most Innovative Companies in 2017

Machine learning is at the heart of all Amazon services, right from its online store to Kindle features. There are several teams at Amazon that depend on Machine Learning, be it the Alexa Engine, Alexa Smart Home Devices, Amazon JHIM,  Amazon Rekognition, Amazon Music , Supply Chain Optimization, Product Graph or Customer Service Personalization. With machine learning power ruling each and every Amazon service, it definitely is the best place to work for all machine learning enthusiasts.

If you have visited Amazon, you might have definitely seen the “Also bought” section where recommendations are provided on which other books /movies you like, based on the books/movies you currently bought/rated. Amazon hires hundreds of machine learning engineers whose job is to tweak this recommendation machine learning algorithm.  

A search for the term “Amazon Machine Learning Jobs” in US as of 21st November, 2017 showed 841 open machine learning jobs on LinkedIn -

Machine Learning Jobs at Amazon

 

What is it like to work as a Machine Learning Engineer at Amazon?

Here’s Gourav Roy’s Answer from Quora on how is it like to do Machine Learning at Amazon -

 

 

Machine Learning Engineer at Amazon

According to Indeed.com, the average machine learning engineer salary at Amazon is $147,554 per annum.

Become a Machine Learning Engineer

If the sky-high salary interests you and would like to prepare for a machine learning interview at Amazon then here are few machine learning interview questions asked at Amazon -

1) Why is it not preferable to use Stochastic Gradient Descent always even though it is faster?

2)  How will you weigh 9 marbles 3 times on a balance scale to choose the heaviest one?

3)  What is the difference between convex and non-convex cost functions?

4) How can you plot ROC curves for multiple classes?

5) What is loss function in a Neural Network?

6) What is boosting?

Read More - Machine Learning Interview Questions and Answers

2) Facebook - Ranked #6 among the Most Innovative Companies in 2017

Automatic Friend tagging suggestions, Personalized News Feed, Mutual Friend Analysis and Friend Suggestions are some of the amazing machine learning products that we use on a daily basis. When any of the 2.07 billion people who use Facebook every month to login to enjoy their personalized news feed or make use of the camera app to share pictures on Instagram, machine learning power is working the background to make it a user friendly experience. 25% of all engineers at Facebook active use machine learning and it is looking to hire more machine learning experts who can help Facebook build the next wave of Machine Learning and AI powered experiences for Facebook users. This is the best chance for all data enthusiasts to work with top industry veterans who are passionate at crunching data. Facebook has 3 AI labs with the newest lab setup in Paris last year, so definitely there are scores of machine learning career opportunities to explore with the social media giant considering its two recent AI acquisitions –Zurich Eye and Masquerade.

A search for the term “Facebook Machine Learning Jobs” in US as of 22nd November, 2017 showed 643 open machine learning jobs on LinkedIn -

Machine Learning Jobs at Facebook

A day in the life of a Facebook Machine Learning Engineer

Machine Learning Engineer at Facebook

According to a Business Insider news Machine Learning Engineer job ranked 4th among the highest paying jobs at Facebook. The average Machine Learning Engineer Salary at Facebook is $124,197.

Facebook Machine Learning Engineer Interview Questions

1) How will you compute the dot product for two sparse matrices?

2) How will you build, train and deploy a machine learning system to identify if a multimedia ad being posted violates any of the terms or contains any offensive material?

3) Google - Ranked #2 among the Most Innovative Companies in 2017

Google has been a powerful force in championing the use of machine learning. Putting machine learning to work has helped engineers crack various difficult cases which were proved challenging since decades. Google has acquired 12 AI and Machine Learning startups in the last 4 years with its major research focus being on machine learning to enhance Visual Processing, Google Language, Search Engine Ranking, Speech Recognition, Image Processing and prediction capabilities.

Why work at Google as a Machine Learning Engineer?

Many people dream of working as a machine learning engineer at Google, bringing home big pay checks, and enjoying the perks of a growing employee-centric firm but there is lots to life than a nice salary and onsite barbershop. The work environment is pleasant, and there are many perks. However, the biggest perk of working at Google as a Machine Learning Engineer is the availability of computing resources. If you want terabytes of data to use, it is available, if you need 20,000 machines, you can get them.

A search for the term “Google Machine Learning Jobs” in US as of 22nd November, 2017 showed 1403 open machine learning jobs on LinkedIn -

Machine Learning Jobs at Google

 

According to Paysa.com, the average Machine Learning Engineer Salary at Google is $158,000 and ranges from $139.000 to $177,000.

Want to work at the “World’s” Most Attractive Employer?  Hone your Machine Learning Skills Now. 

Learn Machine Learning Online

4) Apple - Ranked #4 among the Most Innovative Companies in 2017

Apple is bear-hugging machine learning and has put it in your pocket. The “Apple Brain (Siri)” inside your iPhone is greatly enhanced by the adoption of machine learning. Machine learning is the heartbeat of an iPhone – you see it when the iPhone identifies a caller who is not present in your contacts (but has recently emailed you), when iPhone shows where you have parked your car, even though you never asked, when a location on the map pops up for the hotel that you have reserved much before you type it in.

Apple has acquired four Machine Learning and AI startups within the past two years. And yes, when Apple buys a company, it is usually doing that to hire people with skilled expertise in Machine Learning and AI.

“Apple’s Machine Learning is produced by many people who weren’t necessarily trained in the field before they joined the company. We hire people who are very smart in fundamental domains of mathematics, statistics, programming languages, cryptography. It turns out a lot of these kinds of core talents translate beautifully to machine learning. Though today we certainly hire many machine learning people, we also look for people with the right core aptitudes and talents.”- says Craig Federighi, Senior Vice President of Software Engineering at Apple.

A search for the term “Apple Machine Learning Jobs” in US as of 22nd November, 2017 showed 406 open machine learning jobs on LinkedIn –

Apple Machine Learning Jobs

 

 

The salaries for machine learning engineers at Apple range from $100,097 – $200,000.

It took 10 years for the iPhone to advance into a machine learning supercomputer (iPhoneX). Want to know what will the next 10 years look like, then this is the best time to grab a machine learning career opportunity at Apple.

5) Uber - Ranked #3 among the Most Innovative Companies in 2017

If you thought that Uber is a ride-hailing business, you are wrong. It’s actually a machine learning business. ML is what makes Uber’s service possible –When you request a car, the app tells you that it will take 15 or 20 minutes to show up. It is the responsibility of Uber machine learning engineers to make sure that the estimate is as precise as it can be.

Michelangelo, an internal ML-as-a-service platform at Uber is the de-facto for data scientists and machine learning engineers at Uber. It is built on multiple open-sourced components – Hadoop HDFS, Spark, Tensor Flow, Spark MLib, Samza, and Cassandra. The way forward for Michelangelo as Uber plans to scale and harden the existing system  opens up tons of opportunities for machine learning specialists who can make it more efficient.

A search for the term “Uber Machine Learning Jobs” in US as of 22nd November, 2017 showed 164 open machine learning jobs on LinkedIn –

Machine Learning Jobs at Uber

As per the stats from Paysa.com, the average machine learning scientist salary at Uber is $141,000.

If you are interested in attacking machine learning challenges that push the limits of scale, you should consider applying for a machine learning job at Uber.

Machine Learning is an exciting career and if you can land a job at one of these top machine learning companies then you can enjoy solving difficult math problems, writing code all night long, and spend hours building data visualizations.

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