Welcome to Robociti’s Robotics & AI Career Guide!
This series is a practical guide on the steps required to land the most in-demand positions in the field of Robotics & AI. In this part, I will give a detailed guide on how to start your career in Machine Learning Engineering.
Machine Learning Engineers, like many jobs in AI & robotics, are in high demand. In this post, you will gain an insight to what Machine Learning Engineering is, the average salary of a Machine Learning Engineer, the requirements for the role, and how to get a job in Machine Learning.
Part 1: Data Science Career Guide
Part 2: Machine Learning Engineering Career Guide (This Article)
Part 3: Business Intelligence Development Career Guide
Part 4: Research Science Career Guide
Part 5: Robotics Science Career Guide
Part 2: Machine Learning Engineering Career Guide
Table of Contents
- What is Machine Learning?
- Who is a Machine Learning Engineer?
- Educational and technical requirements to become a Machine Learning Engineer
- Essential soft skills required to become a Machine Learning Engineer
- Getting a job in Machine Learning
What is Machine Learning?
Machine Learning(ML) is the study of computer algorithms that improve automatically through experience. It seeks to get computers to learn and act like humans by learning from data, identifying patterns and making decisions with minimal human intervention. ML is an incredible subset of Artificial Intelligence, used across various industries and professions.
Applications of Machine Learning can be found in our everyday lives with examples such as virtual personal assistants like Alexa and Siri, product recommendations used to show you ads based on your search history, speech recognition to translate spoken words to text, and online fraud detection used by Paypal. These are just a few of the applications driven by Machine Learning.
What does a Machine Learning Engineer do?
Machine Learning Engineering is a relatively new career but it is projected to continue its rise in popularity as more industries begin to understand the value of deep learning technologies. According to Indeed.com, the average base salary for a Machine Learning Engineer is around $141,166/year, and what they do include:
- Developing programs powered with artificial intelligence that allow machines function and complete tasks without human assistance.
- Design and develop AI models.
- Develop new software and systems.
- Engage in data modeling and evaluation.
- Study, research and transform data science models into machine learning algorithms and tools.
- Train and retrain machine learning and deep learning systems for improved performance.
- Develop machine learning applications to user requirements and company specifications.
Most industries that work with large amounts of data recognize the value of Machine Learning. A Machine Learning Engineer can be employed across various industries by obtaining insights from data to make businesses more efficient, gain advantage over competitors and identify new market opportunities. Some of these industries include:
Healthcare: Diagnostics is made more efficient in the healthcare industry by using ML to analyze trends in patients’ health data. It has the ability to detect medical problems much faster than humans and improve diagnostics and treatment.
Government: Machine Learning is used in various government sectors to organize and analyze multiple sources of data that can be mined for insights. It can also help detect fraud and minimize identity theft.
Financial Services: Businesses in the financial industry such as banks, use machine learning to prevent fraud, identify investment opportunities and gain insights in data.
Transportation: Machine learning is used in traffic regulation to predict, monitor and manage traffic. Companies such as Tesla are using ML algorithms in the test runs of their self-driving cars.
Retail & Customer Service: Retail companies use machine learning algorithms to make personalized recommendations that show consumers their favorite products.
Educational & technical requirements needed to become a Machine Learning Engineer
Many employers require Machine Learning Engineers to have an advanced degree such as a Masters or Doctorate degree in a related discipline like Computer Science, Data Science, Math, Statistics or Software Engineering.
The core technical skills required by employers for a Machine Learning Engineer, include computer programming skills, mathematical skills, knowledge of cloud applications and computer languages listed below. Learning these skills can have an impact on the salary of an entry level Machine Learning Engineer.
- Math & Statistics
- Data Modelling & Evaluation
- Big Data Analytics
- Computer Vision
- Data Analysis
- Deep Learning
- Image Processing
- Natural Language Processing
- Software Engineering
- Machine Learning Algorithms and Libraries
- Software Development
As mentioned above, in order to enhance your ML skills, you will need to get familiar with common algorithms, platforms and libraries that are used in Machine Learning to make work more efficient. Based on current job postings, they include: TensorFlow, WEKA, RapidMiner, MATLAB, Fusioo, KNIME, Apache Mahout, BigML, Paxata, ML Jar, Tableau and Google Cloud AutoML.
The best way to get familiar with these tools is to participate in online Machine Learning challenges and get involved in related communities.
Essential soft skills needed to become a Machine Learning Engineer
Apart from the technical skills that you need to become a Machine Learning Engineer, recruiters require ML Engineers to have these important set of soft skills.
- Good communication skills: important when sharing your findings with others, including those who do not understand Artificial Intelligence.
- Critical and creative thinking: helps in problem solving abilities and helps in looking at things from a fresh perspective.
- Work ethic: This is a set of values based on ideals of discipline and hardwork. Creating good habits such as staying motivated and finishing tasks promptly is an important part of being successful in your career.
- Team work: A ML Engineer usually works with other engineers, and other non-tech people. It is important to work well with others, leading to better productivity in the workplace.
- Time Management: the ability to prioritise and assign the right amount of time to your tasks is very essential in your job as an ML Engineer.
You can develop these soft skills by expanding your learning mindset and encouraging self-reflection. You can also get feedback about your soft skills from other people.
Getting a job in Machine Learning
When searching for a job online in Machine Learning, it is important to join some online job platforms that can help connect you with recruiters. Some of these sites are job boards that focus on programmers, engineers and developers. Below are some of the top sites to use:
You can also find job opportunities by networking with existing machine learning engineers to learn about their team and hiring practices, or attending machine learning events.
Once you’ve secured an interview, you’ll want to get prepared for it. There are many resources online to help you prepare for the interview process. You should expect to receive some general questions on machine learning theory.
You can impress hiring managers or recruiters with your proven projects, this will give you an advantage over other applicants and get a direct line to hiring professionals. After you have been hired, you’ll want to learn exactly what your goals are and what your role within the team is.
Machine Learning is a relatively new field and is still evolving. To gain an advantage when applying for jobs, you have to constantly practice the skills you have learnt and also develop new skill sets. You have to stay updated with the latest tech trends, work with new tools, read scientific journals, and get involved in online and offline communities.
Various Machine Learning applications are already impacting our world. This is the time to brace yourself for the future with Machine Learning!