Enticed by machine learning engineer jobs?

Over the past few years, the limelight on machine learning or ML has dramatically increased.

This aspect of the tech world has found its way into plenty of other industries.

Although the concept of machine learning has been prevalent for a long time, the world is just beginning to see real advancement in ML.

Be it transportation, advertising, retail & e-commerce, or healthcare, all types of sectors are now heavily dependent on ML and AI.

Tesla’s autopilot car? Product of machine learning.

Facebook ads? Yep, the social media site uses machine learning to produce the approximate action rate and the score for ad quality.

Not to be confused with AI, (an umbrella term with a loose specification) machine learning is a specific aspect of artificial intelligence.

The basic difference between artificial intelligence and machine learning engineering is that AI systems don’t need to be pre-programmed as they use algorithms that can work independently.

Whereas, machine learning uses an algorithm dependent on historical data and works only on domains for which it is programmed.

machine learning engineering job trend

Source: Databridge

Given the steep rise in the importance of ML in today’s world, it is only apparent that machine learning engineering jobs will be on a steady rise simultaneously.

How to Become a Machine Learning Engineer?

The work of a machine learning engineer is quite technical.

One cannot expect to start a career in this field without having a bachelor’s degree in computer science, mathematics, or any related field.

Follow these tips to start your career as a machine learning engineer:

Polish Your Data Science Skills

Since an ML engineer is responsible for creating machine learning algorithms that fulfill the desired purpose of the program without human intervention, data science skills are a necessity.

Being proficient with programming languages like Python, R Programming, Java & JavaScript, Julia, LISP, etc facilitates the work of an ML engineer.

If the aspirant is not fluent with programming, there are various open-source ML environments such as Google Colaboratory, IBM Watson, Orange, BigML, etc that allow implementation of ML algorithms without the need for steadfast coding.

However, having a fundamental understanding of programming & coding is a necessity for grabbing machine learning engineer jobs.

Work on Your Software Engineer Skills

Possessing a fusion of mathematical, statistical, and coding skills are vital for understanding the basics of computing like deadlocks, clusters, variance, memory, and so on.

The aspirant has to be capable of working with data structures like multidimensional arrays, trees, queues, stacks, etc.

Get Familiar with Machine Learning Algorithms

The candidate must also have experience with using the four types of machine learning algorithms that are listed below:

  • Supervised Learning
  • Semi-supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

machine learning engineer algorithms

Source: Research Gate

Some of the commonly used machine learning algorithms are mentioned in the following table:

Linear Regression Logistic Regression
Decision Tree SVM
Naive Bayes kNN
K-Means Random Forest
Dimensionality Reduction Algorithms Gradient Boosting algorithms

Build a Project Portfolio

While applying for machine learning engineer jobs, a machine learning resume is not enough to effectively showcase the skills of an ML engineer.

Given the tech-savvy nature of ML job profiles, employers would naturally expect candidates to have polished online portfolios.

Kaggle

Kaggle is a vast online community of machine learning engineers and data scientists with over 536,000 active members from all over the world.

The platform allows users to compete in solving real-life problems to increase their Kaggle rankings & win prizes.

It lets users build prototypes in web-based data science environments and learn from fellow engineers and data scientists.

Participating in Kaggle competitions is a great way to gain some hands-on experience in machine learning projects.

GitHub

Github is a code hosting platform that allows fellow ML engineers and data scientists to collaborate and work together on projects.

It helps users to keep a track of their work and progress whilst enabling them to store & maintain all the codes that they write.

Basically, it documents all the work that users do and serves as an online portfolio.

Work on Personal Machine Learning Projects

When you’re first starting out, you can try to work on personal projects that will not only help you implement your machine learning knowledge to practice, but will also serve as an addition to your resume and portfolio.

You can recreate and review basic projects using free software machine learning libraries like Scikit-learn, PredictionIO, Awesome Machine Learning, and others.

Try to build simple AI algorithms from scratch to make the best out of personal projects and hone your machine learning skills.

Find Internships

Another way to really get yourself ready for machine learning jobs is by doing internships.

Internships will provide you with a clear understanding of how machine learning works in the professional world.

You can get a chance to learn business-specific machine learning skills which you will not get through personal projects or by joining online communities.

What does a machine learning engineer do?

Generally speaking, a machine learning engineer automates a certain process or system by programming an algorithm that predicts results based on established data.

The work done by machine learning engineers is a part of almost every aspect of today’s world.

Social media can be taken as the fundamental product example of machine learning.

From the “people you may know” suggestions in Facebook to everything that users see in their explore page in their Instagram account can be taken up as examples of machine learning.

Whatever the users often interact with, they’ll see more of such posts popping up on their social media pages.

The algorithm created by machine learning engineers is programmed to show data based on the user’s usage pattern.

This is just a basic instance to understand the work done by machine learning engineers.

The common tasks of a machine learning engineer are given below:

machine learning engineer responsibilities

Machine Learning Engineer Jobs Salary

An ML fresher with no prior experience can earn about $93,412 per annum.

While machine learning engineers with 9+ years of experience in this field can earn about $149,583 in a year.

machine learning engineer jobs salary

Source: PayScale

Apart from prior experiences, the salary of machine learning engineer jobs depends on other factors like location and company.

A few of the US cities with the highest pay scales for machine learning engineer jobs are Cupertino, San Francisco, and Santa Clara CA, among others.

Depending upon the company, ML engineers also enjoy perks and incentives such as flexible schedules, computer assistance, visa sponsorship, health insurance, and so on.

Machine Learning Engineer Jobs

With the indispensable work done by machine learning engineers for companies from all sectors, it is not a surprise that ML engineers are being hired left and right.

One simply needs to know where to look.

Entry Level Machine Learning Engineer Jobs

COMPANY JOB LISTING
Jerry Listing
PPL Corporation Listing
Govini Listing

Senior Machine Learning Engineer Jobs

COMPANY JOB LISTING
Dice Listing
TikTok Listing
SmartBiz Loans Listing

Machine Learning Engineer Jobs for Highly Experienced Candidates

COMPANY JOB LISTING
Amazon Web Services Listing
Comcast Listing
Wayfair Listing

Apart from the mentioned job listings from different sectors, keep an eye out for machine learning engineer job listings by companies like Apple, Amazon, Microsoft, Google, and many others who are constantly hiring.

Checking the targeted company’s career page on their websites and LinkedIn pages from time to time helps to keep candidates up to date with ML vacancies.

Start applying for these machine learning engineer jobs with a stellar resume and prepare in advance for the interview.

Making a Career Switch to Machine Learning Engineering

Given the high pay scale and various benefits enjoyed by machine learning engineers, many people try to make a career switch to ML job profiles.

The good news here is that it is indeed possible to do so.

Provided the aspirant meets the basic educational and certification requirements.

The first thing that the candidate must do is familiarise and polish their software skills.

There are plenty of online certificate courses and training which provide just that.

A few of the courses and online certification programs are listed below:

  • Machine Learning with TensorFlow on Google Cloud Platform Specialization
  • Elements of AI by Helsinki University
  • Intro to Artificial Intelligence by Udacity
  • Professional Certificate Program in Machine Learning and Artificial Intelligence
  • Machine Learning – Stanford University (Coursera)
  • Machine Learning with Python
  • Machine Learning Crash Course - Google
  • Professional Certificate in Foundations Of Data Science
  • Data Science and Machine Learning Essentials – Microsoft (EdX)

Machine Learning Career Path

The career path of a machine learning engineer can indeed be a very fulfilling one.

If you’re passionate about working with data and algorithms, these job profiles aligning with the career path of a machine learning engineer can be of interest to you:

Data Scientist

The role of a data scientist largely revolves around analyzing data to identify valuable information that helps businesses and companies achieve their goals.

They are also responsible for sourcing reliable data, identifying areas of improvement, and communicating their findings to the stakeholders in a comprehensible manner.

Candidates aspiring to become data scientists must possess skills like:

  • Machine learning
  • Programming
  • Database management
  • Data visualization
  • Predictive modeling
  • Statistics and so on

NLP Scientist

NLP stands for Natural Language Processing and as an NPL scientist, you will be creating devices and systems that are capable of understanding human language.

They create intelligent machines that can learn speech patterns and process speech to even translate them.

Come of the common duties of an NLP scientist includes:

  • Designing data science prototypes including NLP applications
  • Developing algorithms to process and implement advanced text speech
  • Using machine learning to process speech and air transport
  • Creating and executing OCR and cognitive data extraction

NLP scientists are adept at feature extraction techniques such as PCA, EEG, MFCC, etc.

Business Intelligence Developer

A business intelligence developer is responsible for implementing data analytics to create software or tools that facilitate the formulation of business strategies.

They often work in collaboration with data scientists and engineers to develop intelligence projects that support business decisions and maintain BI interfaces.

Business Intelligence developers possess a competent knowledge of data warehouses, data mining & analysis, programming languages, and so on.

Human-centered Machine Learning Designer

As the name suggests, human-centered machine learning is centered around humans.

It caters to the needs of humans and resolves technology-related issues faced by them.

The automatic movie recommendations shown by Netflix, amazon prime, and other streaming service providers can be taken as an example of human-centered machine learning.

Human-centered machine learning designers basically develop smart systems that can learn the preferences and identify the user's patterns.

A few of the key skills of a human-centered ML designer are:

  • Programming languages
  • Data structures
  • Algorithm techniques
  • Testing and troubleshooting

Salient Features of the Blog

  • Machine learning engineers design algorithms that is dependent on historical data and produces results that are based on it
  • AI is a broad term used to signify the entire artificial intelligence industry while machine learning is a term used to denote a small part of the same
  • Almost all the sectors like finance, transportation, retail, e-commerce, healthcare, etc are dependent on machine learning
  • Data science and software skills are a must for machine learning engineers
  • GitHub and Kaggle are two of the most popular online portfolios that machine learning engineers must maintain
  • A few of the responsibilities of a machine learning engineer includes implementing ML algorithms, verifying data quality, analyzing statistics and so on
  • California is one of the US cities with the highest pay scales for machine learning engineer jobs
  • Aspirants of machine learning engineer jobs can make a career switch to this field if they have the necessary educational background & training

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