Machine learning is pivotal for today’s business. And we’ll share some helpful insight on how to find the right expert.

Code Against the Machine

Machine Learning (LM) is gaining momentum even faster now. As the Stanford’s machine learning course showed, hundreds of thousands are interested in it.

So, if you want to hire machine learning developers, you need to understand which skills and knowledge they must have.

We prepared a quick guide that explains what an ML developer or a data scientist should be capable of.

1.Hard Skills

Applied math

It all starts with mathematics. For a machine learning expert it’s a foundation. It is responsible for:

  • Matrix operations.
  • Approximating confidence levels.
  • Manipulating complex data structures.
  • Understanding orthonormalization, eigenvalues, vector spaces, and scalars.

Besides, ML heavily relies on statistical modeling procedures. Without them, it’s impossible to create algorithms.

So check if the candidate is proficient with linear algebra, multivariate calculus, probability, distributions, statistics, etc. Knowledge of physics is also a big plus.


Data analysis, collection, storing, or distribution are impossible without algorithms. Machine learning algorithms are the chief “power move” of the entire thing.

In most cases, we deal with the three main types of these algorithms:

  • Supervised

A machine learns by studying examples. This type allows it to understand how to achieve certain input/output results, using the algorithm. This type comprises Classification, Regression, and Forecasting as the key learning elements.

  • Unsupervised

This type is dedicated to learning and understanding patterns. In this scenario, the machine works “solo”, detecting correlations and relationships. In turn, this info comes from the offered data sets. This type comprises Clustering and Dimension Reduction.

  • Reinforcement algorithms

This type is based on regimented learning processes. In essence, the machine digs through a bunch of parameters, values, and actions. In the end, it learns enough to find the best results and solutions.

And the popular algorithms include K Means Clustering, Linear Regression, Artificial Neural Networks, Random Forests, Nearest Neighbours, and others.

So, if you are planning to hire an AI developer, definitely check what their resume has to say about algorithms.

Computer science

Another essential requirement. Computer Science and its key concepts — space/time complexity or data structures — play an important role in data science.

For instance, it helps to work with the underlying patterns that lace the data. And if you look at the data structures, you’ll realize that its concepts like queue, tree, or stack are imperative for organizing vast amounts of info.

Another essential aspect in ML and AI development is the parallel computing. The trick is that today data expands very fast.

So, parallel computing can solve many issues: decreasing errors, creating scalable databases, applying intelligent data caching, real-time updating, and so on. A top machine learning must surely excel at CS.

Data Modeling

Data modeling is another skill to expect. When you need to hire AI & ML developers!  It is responsible for finding and retrieving patterns that aren’t always visible to a humanbeing. Even a trained one.

Modeling together with evaluation helps to do that. Your candidate must be capable of choosing a classification algorithm that suits a specific bulk of data. And the categorical variables.


Programming languages are critical for ML. By knowing them, a developer can tackle real-life business problems: solving logistical issues, making a prognosis, detecting trends, and so on.

Typically, a machine learning dev is expected to know:

  • R Programming. A popular open-source language that is also a great analysis tool. All thanks to its graphical nature.
  • JavaScript. JS isn’t for app developers only. It’s also used for making ML libraries that help prevent fraud and cyberattacks.
  • Julia. It is a dynamic language often used for making ML apps. The ML area benefits from its numerous perks: compiler, numerical precision, distributed parallel execution, and so on.
  • Scala. It offers a static type system. In turn, it is compatible with Java-powered frameworks. Plus, its USP is great for making big data apps that contain a monstrous amount of data.
  • Lisp. It has useful features that make prototyping and facilitating novel objects much easier. It even has a garbage collector for the junk code.

Other recommended languages are TypeScript, Golang, Python, Shell, and others.

So, if an AI developer for hire mentions some of these in their resume — it is a good sign.

Natural Language Processing

Natural language processing or NLP is crucial. In simple terms, it teaches the computer to understand human language. With all its blunders, dialects, misspelling, accents, jargonisms, and so on.

It is necessary for improving the Human vs. Computer communication. It can be done with the help of special libraries — they contain syntax rules for the computer to understand.

Among the top libraries are: Gensim, Natural Language Toolkit, TextBlob, PyNLP, etc. Definitely a must for an AI engineer.

Neural Networks

Machine learning won’t function without sequential, parallel computations and other vital components. In essence, they provide data analysis that helps the machine learn and get smart in real-time.

A neural network is the answer. Here is a caboodle of those that can be used in ML: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Feed Forward Neural Network (FFNN), Adversarial Generative Network (GAN), and many others.

The job seeker doesn’t need to know all of them. It’s enough if they understand the core concepts. And explain which tasks and how they’re going to solve them with a neural network.

Computer vision

Every time a computer sees a real-world item and identifies it, it is computer vision. No matter if it’s a military-grade robot or just your phone’s camera.

Computer vision is rising in popularity. Especially with the advent of the Internet of Things (IoT).

You may want to integrate it into various software solutions — from a tourist app that can detect an architecture style of a baroque mansion to a program that is “eyes” of a domestic robot.

Whatever it is, knowledge of how computer vision works is crucial.

  1. Soft Skills


As usual, no worthy product can be made without it. While software engineering is meat and potatoes, communication is a gravy.

You need an ML dev who is willing to listen to advice. Who isn’t afraid to voice their opinion and propose solutions.

Who feels comfortable when providing and collecting feedback. And who also knows how to convey their idea via storytelling.

Besides, good communication is like oxygen for a healthy work atmosphere. With it, your team can achieve impressive results.


An ML engineer should be creative. First, the area requires unorthodox thinking and bold solutions.

Second, such an engineer can “inject” some of their creativity into the way the machine will learn. This achieves amazing results.

What Else to Know?

What else to know when you hire artificial intelligence developers? We’ve prepared some top interview questions!

1.What is decision? And what is recall?

2.Why is deep learning different from ML.

3.Name the different machine learning types.

4.How does likelihood differ from probability.

5.Describe data pipeline building step by step.

6.What’s the meaning of Bayes’ Theorem in ML.

7.Why are L1 and L2 regularizations different?

8.Specify the trade-ff between bias and variance.

9.When would you use logistic regression model?

10.In which scenario would you use cross-validation.

The Killing Machine

Machine Learning isn’t a vague, geeky term anymore. It’s  a new reality in the world of business, science, and even public life.

It helps extract valuable knowledge and insights from millions of GB of data.

It can even work as a time machine, predicting the future to an extent. And helping companies, researchers, and administrators foresee the upcoming challenges.

With our help, you can hire a machine learning engineer with years of experience. Local or foreign, freelance or full-time — you set the rules!