With so many companies investing in machine learning, it seems clear that soon businesses without some ML features implemented will stay way behind their competition. Thanks to the rapid developments in the sector during the recent years, powerful data usage can now be incorporated in nearly every industry, with top machine learning use cases being risk management, performance analysis and reporting, trading, and automation.
Do you want to follow the path of the most successful IT giants and startup unicorns and invest in ML as well? If so, you will need to finally hire machine learning engineers to build for you the algorithms you need.
But how to make sure you really hire the best and the most fitting devs on the market? Here is our Ideamotive ultimate guide to hiring machine learning software engineers.
What does a machine learning engineer do?
Machine learning software engineers, although at first glance working similarly to other developers, have actually a bit different objective. Their job is, in the end, to build algorithms that are able to learn and develop their skills by themselves, with minimum involvement of a human.
An example? AI-powered data analytics software. Machine learning engineer’s task is to make this software able not only to read the data and provide a summary of it, but also “teach” it to decide which data is really worth considering and even take data from other available sources.
Another example could be a voice reading tool. Machine learning engineer could feed the AI software with thousands of hours of videos in multiple languages and make it recognize the differences between each language.
If you plan to automate routine tasks at your company, hiring machine learning engineers to build an AI-power solution would be your best bet.
The most important machine learning engineer skills
Machine learning engineering is a discipline at the intersection of data science and software engineering. It's a critical role in today's data-centric world, and as such, hiring managers are looking for candidates with a robust set of skills.
One of the most sought-after skills in a machine learning engineer is proficiency in programming languages. A solid grounding in Python, R, or Java is a must-have, as these languages are often used to implement machine learning algorithms and build models.
Knowledge of libraries and frameworks like Scikit-learn, TensorFlow, and PyTorch is crucial as they provide the tools for developing and deploying machine learning systems. These programming skills should be complemented with a strong foundation in computer science fundamentals, data structures, and algorithms.
In addition to these technical skills, a machine learning engineer should also possess a strong mathematical background.
An in-depth understanding of statistics and linear algebra is crucial for understanding the mechanics of machine learning algorithms and creating accurate predictive models.
This mathematical foundation enables the engineer to understand and optimize the performance of these algorithms and to interpret their results accurately.
Experience with cloud platforms like AWS, GCP, or Azure is also highly valued, as these platforms offer powerful tools for managing and scaling machine learning workloads. Finally, familiarity with data visualization tools is important to communicate complex data and model results effectively.
All these skills combined with a problem-solving mindset and ability to work in teams make a machine learning engineer an invaluable asset to any organization navigating the digital landscape.
What makes machine learning engineers great team players?
One of the aims of machine learning is to build software that will make computers work more effectively than humans. With the help of a well-built algorithm, a machine can do a specific task multiple times faster than a human - with the quality of the final outcome being very similar.
To get it done, however, machine learning software engineers themselves have to work nearly like a machine, being prepared to handle the development process with as few issues as possible.
Because of this, when looking to hire ML engineers, you should look not only for specific technical skills, but also personality features, such as:
- Desire to always learn new things
There is still a lot to discover and improve in terms of AI and machine learning. Following news, trends, and trying to influence those, is something that turns engineers from craftsmen into experts who revolutionize their industry.
- Being proactive
Machine learning engineers are often alone in their work — there is simply no one around that understands the concepts they work with on a daily basis. This means they have to be proactive, choosing the best solutions and coming up with interesting and unique ideas that no one necessarily asked them about.
- Being detail-oriented
Machine learning development is an incredibly complex process that requires complete focus on every stage. Without the full attention of a developer, software-breaking bugs and issues happen more often, making the launch date delayed.
- Patience
Firstly, it takes a long time to develop a good machine learning algorithm. Secondly, it takes a while before the computer learns enough to give you the results you expected. There is a lot of waiting and observation involved in machine learning development, and both the engineer and the non-technical company members should be aware of this beforehand.
Machine learning engineer job requirements — always know your goal
Whether you run a robotics company, a manufacturing plant, or a startup with a mobile app as the main product, there is always a process that can be improved by machine learning engineers. You should watch out, however, who exactly you hire for the role — a machine learning engineer shouldn’t be considered a generalist, but rather as a developer specialized in a specific type of ML programming.
This means, that when you actually start the hiring process, you should look for machine learning engineers who fit your company and project as much as possible. Look for those who have previously worked in the same industry as yours and on ML functionalities that seem to be similar to what you have been planning for your company. To get this done accurately, remember to specify what exactly your machine learning product is supposed to be. If possible, discuss your needs and goals to be achieved with software consultants or IT project managers to understand exactly who you must be looking for.
With this in mind, you’ll end up hiring machine learning software engineers who will deliver the required solutions fast and to the highest quality.
Best job interview questions to ask a machine learning engineer
After you have managed to review candidates and created a shortlist of those who you consider to hire, you can start running interviews. They are done partly, of course, to assess whether the developer fits your existing team: what’s their motivation to work for you? How do they fit your overall company culture? There are many questions to ask for which there are usually no good and bad answers — it’s up to the interviewer to decide how is their desired hire like.
But there is also the technical interview that must take place. If you are not a developer yourself and you don’t understand all the complex theories behind machine learning and artificial intelligence, you might consider asking someone for help in running this part of the interview. The ideal choice would be another machine learning engineer or an IT Project Manager experienced with ML projects. If you don’t have anyone like this at your company yet, think of your colleagues/friends, or book some time with an external consultant to help you out.
If you want to make the hiring process even faster and more efficient, reach out to us at Ideamotive. Our team will find you the best candidates on the market, tailored specifically to what your business needs in the long run.
Whatever your final setup will be, here are some machine learning engineer interview questions that might be asked:
- How is the k-nearest neighbor algorithm different from k-means clustering?
- In what case Ridge regression is considered better than Lasso regression?
- What is data binning and what are the differences between fixed-width binning and adaptive binning?
- What is convex hull?
- What kind of algorithm is used to build Amazon-like e-commerce recommendations? ("Customers who viewed this item also viewed", “What other items do customers buy after viewing this item?”, etc.)
- What are the methods of handling outliers? Name and explain all of the ones you know about.
- What is the Curse of Dimensionality? How to overcome it?
- What is naïve Bayes? Why is it called “naïve”?
Making the most out of your machine learning solution
Machine learning engineers are surely experts in their field, but they might not have the skills necessary to take care of the less technical aspects of machine learning product development. To make sure you make the most out of your machine learning solution, consider hiring also for roles like project manager, product owner, or business analyst.
The most efficient way to get all these experts on board is to contact our team at Ideamotive. We’ve developed an extensive network of top IT talents from all over the globe. Whether you need a machine learning developer, an AI expert, or other professionals to join your project, we have the people you need.
