A day in the life of a machine learning engineer
22/12/2021 by MRL
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Technology is being developed at an increasing rate. The more skilled minds focus on this industry, the faster we will continue to see developments. If you want a job where you can play an active role in making history, becoming a machine learning engineer is a fantastic option.
Here’s everything you need to know about the position.
Machine learning, or ML, is similar to being a teacher, only much, much cooler. Instead of teaching humans, you'll be training machines, programming them to continue learning by themselves without any input from humans.
Machine learning sits under the umbrella of Artificial Intelligence (AI) and is a fascinating field to be working in at the moment.
There are multiple sectors where machine learning engineers are essential. That’s great news because it means that you can build your career while focusing on something you love.
Do you have a flair for automotive? You could look for jobs in autonomous driving. Alternatively, you could land a job working for the likes of Google, or Bing, helping them develop their web search platforms. Or perhaps you want to build your own search engine to rival these giants and be your own boss; all of these are possible through machine learning.
Firstly, let's take a look at the responsibilities you’ll likely see on a job description, then we’ll run through what a typical day may look like for a machine learning engineer:
Analysing business objectives and developing models in line with them.
Comparing data distribution and identifying differences.
Defining validation strategies.
Managing hardware and data as resources.
Reviewing machine learning algorithms as a problem-solving tool.
Supervising data acquisition processes.
Verifying data quality through data cleaning.
Every machine learning engineer starts their day reviewing the codes and projects previously set up and running, comparing them to baseline models and ensuring they are working as predicted.
They will then write code or design databases and concepts to assist with new and emerging projects or build on existing projects to improve performance. Daily tasks will also include testing models and writing units.
They will also attend meetings with higher-ups or clients they are working with to give feedback on projects and pitch new product ideas.
A machine learning engineer will also be on hand to respond to code requests and any problems raised out of hours that require immediate attention. Because of the nature of this work, an ML engineer job role is not strictly 9-5, and out-of-office hours should be expected.
In addition to the qualifications we’ll discuss in the next section, candidates looking for an ML engineering role will be required to showcase the following hard skills:
Ability to work with probability and statistics, and a working knowledge of concepts such as Bates Nets, Markov decision processes etc.
Confident in working with software engineering and system design.
Familiarity with ML algorithms and libraries.
Understanding of data structures, computer architecture and algorithms.
Soft skills that employers look for when hiring a machine learning engineer include:
Excellent verbal and written communication - including explaining complex concepts and processes to people who aren't directly involved with programming.
Excessive attention to detail.
High levels of innovation and creativity.
Robust analytical skills.
Strong mathematical skills.
As you progress into more senior positions, you’ll likely have to demonstrate the following:
Competence with using infrastructure as code.
In-depth understanding of Python, Java and C++.
Knowledge of ML evaluation metrics and best practices.
Linux SysAdmin skills.
Management and leadership skills - for both projects and teams.
Don't worry if you're interested in becoming an ML engineer but didn't study machine learning at degree level. Because this is such a new discipline, you'll rarely find a course that deals solely with this subject.
You will, however, be expected to hold an undergraduate degree in a relevant field of study. The following are widely accepted as sufficient training before moving onto a Masters or PhD:
Most companies looking to employ a machine learning engineer will expect the successful candidate to hold a Master's degree or PhD that has at least a partial focus on machine learning.
You may also be required to obtain some field experience.
If you don't hold any of the undergraduate degrees mentioned above or masters or PhD but still want to pursue a career as an ML engineer, there are other options.
Perhaps you’ve already had a fulfilling career in statistical or data analysis, for example, but are looking for something new; you could transfer those skills towards a position in machine learning.
In a situation like this, you can take a course in machine learning to help you step into your new career path. If this is similar to your situation, we'd love to help where we can.
Why not get in touch, and we can speak to our network about finding a position for you?
MRL operates on a global scale, helping talented people find jobs where they are located. The average salary you can expect as a machine learning engineer will differ depending on the country you plan to live and work in.
Here’s a quick rundown of average salaries by country:
Average ML engineer salary
However, please remember that this will fluctuate depending on the role's location within your chosen country.
Without a shadow of a doubt, yes. Because the technology is so new, this is a fantastic opportunity to get on board and play your part in making history and driving the industry forward.