Machine Learning Engineer Salary – Is it Good or Bad?

Machine Learning Engineer Salary – Good or Bad?

Machine learning is fast becoming an extremely popular field of computer science to work in, and companies are demanding for machine learning engineers every year, this is a great opportunity.

So what’s machine learning engineering?

In my own words, machine learning is an application of artificial intelligence (AI) that provides systems the ability to learn and improve from experience without being explicitly programmed.

It focuses on the development of computer programs that can access data and use it to learn for themselves.

In this digital age, computations are becoming lightning fast and we are heading into an era where computers will be doing most of our work, machine learning and artificial intelligence have a huge scope and deserves your attention.

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Who’s a machine learning engineer and what does he/she do?.

A “machine learning engineer” is someone who engineers machine learning systems.

This includes the core algorithms, but also the supporting information infrastructure particularly in deployment scenarios.

Programming ability becomes substantially more important than for a data scientist.

The ability to read and digest research papers along with considerations of architecture and systems becomes important.

Machine learning engineer salary californiaMost machine learning engineers are not really implementing new algorithms, just old algorithms in new environments.

The role of a machine learning engineer is actually much better defined than that of a data scientist.

Simply because companies using that job title are the ones that have a very clear idea about how and why they want to utilize machine learning.

Additionally, these companies almost have data scientists as well, so they have defined the distinction between the two.

Data scientist is a job title that’s often poorly defined. It’s usually an analyst that knows some programming and machine learning.

A machine learning engineer is a full-blown software engineer that has specialized in machine learning.

The most important responsibilities of a machine learning engineer are roughly these:

  • Running machine learning experiments using a programming language with machine learning libraries.
  • Deploying machine learning solutions into production.
  • Optimizing solutions for performance and scalability.
  • Data engineering, i.e. ensuring a good data flow between database and backend systems.
  • Implementing custom machine learning code.
  • Data science, i.e. analyzing data and coming up with use cases.

How do you become a good machine learning engineer?.

First, make an effort to understand the rudimentary concepts of machine learning and the very origin of it.

If you want to out-master the tech, you need to go deep down to the roots, before penetrating into stem and branches.

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For you to be a good machine learning engineer. Here is a list of must-have traits:

  • You need to enjoy an iterative process of development. If you want to build a machine learning system, you need to be able to build a version 0.1 using a very simple model quickly. Then iterate on getting it better at every successive stage.
  • You also need to have a good intuition for when to stop. In any machine learning system, you can always improve the accuracy by iterating on it more. But at some point, the effort you put into it exceeds the value you derive from it. You need to be able to identify that point.
  • You should be comfortable with failure. A lot of your models and experiments will fail. You should be driven by curiosity. The best people are the ones who are genuinely curious about the world around them and channel that curiosity when working on machine learning.
  • You need to have a good data intuition.
  • You should be good at identifying patterns in the data. Being able to create quick data visualizations using Python, Matlab or Excel helps.
  • You need to have a good sense of metrics and be metrics-driven.
  • You should be able to establish metrics that define success or failure of your system.
  • You should feel comfortable with blind experiments and terms like precision, recall, accuracy, ROC, conversion rates, NDCG etc.
  • You should be able to develop a generalized approach to fixing the bugs in your models.

Perhaps you’ve applied core concepts in the workplace or maybe you already have the experience and want to take your artificial intelligence expertise to a higher level, an online machine learning course can equip you with the tools needed to understand the basics or accelerate your career.

Many institutes like Henry Harvin and Andrew Ng are offering detailed machine learning courses that can benefit you in your future.

Here is a list of machine learning engineering courses for beginners:

  • Algebra
  • Probability and Statistics
  • Basic algorithms and data structures
  • Python programming

To increase your chances of succeeding in the advanced level program, I highly recommend that you have the following prerequisites:

Upgrading to a higher level in machine learning, you must enroll in andrew ng’s advanced machine learning courses:

  • Machine Learning
  • Deep Learning
  • AI For Everyone
  • Neural Networks and Deep Learning
  • Sequences, Time Series and Prediction
  • Introduction to tensor flow for Artificial Intelligence, Machine Learning, and Deep Learning
  • Structuring Machine Learning Projects
  • Tensor Flow: Data and Deployment
  • Sequence Models
  • Improving Deep Neural Networks: Hyper parameter Tuning, Regularization and Optimization

So, if you have decided to add machine learning engineering to your resume or curriculum vitae, then the above mentioned courses are the perfect choice for you.

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Career Benefits:

  • You become eligible for machine learning jobs demanding background.
  • Distinguish your profile from peers during job interviews.
  • Open doors to job opportunities abroad demanding specialization.
  • Upgrade business card with Hallmark of Global Credential – CMLP Professional added next to your name.

Now what’s the salary of a machine learning engineer?.

In most high tech companies as a data scientist you start off with around a 40,000 euros per year and progress gradually.

For senior positions, salary ranges between 85,000 to 200,000 euros if you do well.

You can of course create your own start-up and become a CTO, that would pay you a lot more money but I doubt whether in such a position you can work on machine learning algorithms all day.

Why do they earn high salaries?.

Not all but the good machine learning engineers earn high salaries, for two main reasons:

  • There is currently extreme undersupply of talent in the market, and this trend will last for years.
  • Their work can potentially generate high value, as machine learning or AI is one of the main forces transforming our society into future.

Final Thoughts

Machine Learning is becoming one of the most interesting and fast-paced computer science fields to work in.

Also, with every industry looking to apply AI in their domain, studying machine learning opens world of opportunities to develop cutting edge applications in various verticals, such as cyber security, image recognition, medicine, or face recognition.

With several companies on the verge of hiring skilled ML engineers, it is becoming the brain behind business intelligence, making it a course that you wouldn’t like to miss.

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