How to Hire Machine Learning Engineer

According to Indeed, a position of machine learning engineer 2019 has the highest demand growth of 344 percent in the number of job postings. That makes talent acquisition in this scope pretty competitive.

8 min read, Jul 23
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Such boost is implied by the acceleration of digital transformation within logistics, retail and other fields which basis is comprehensive data analysis. The amount of data required for collecting and processing as well as the difficulty of necessary calculations for reporting and forecasting don't allow performing these tasks manually.

Machine learning models after certain “training” are able to execute their tasks independently and what’s even more valuable update their “knowledge” with time and form predictions for the following examination results.

Business process automation is not just an option anymore. In addition to the all-time goal of cost-efficiency, the market demands an increase in speed and visibility. Such tendency is the most apparent in the scopes working directly with end-users like retail and eCommerce. ML algorithms allow defining weak spots in workflow and create digital solutions for their optimization.

Using AI approaches in strategic planning gives the ability to raise the accuracy of the definition of the current market and company state and forecasting of the future operating conditions. It allows changing business running on the core level enabling the further development even of such conservative fields as logistics and supply chain.

Hire machine learning expert or data scientist

Before going headhunting the problem that a machine learning programmer is hired solve should be defined. This way HR will have the opportunity to distinguish candidates with the appropriate experience in advance saving timing and financial resources.

To work efficiently the one must have access to data flows the number and relevancy should be determined in assigning tasks. Unlike data scientist machine learning engineers look more into variables dynamics than values and their meanings since the main task is the given process automation.

That’s why a job description for ML experts is less similar to the one for a statistician and usually more focused on skills concerning computer science and software engineering. But it still includes high demands for knowledge of maths, statistics, and probability.

Machine learning and data science as well as other AI subsets like image processing, deep learning, NLP, etc. have overlaps in knowledge basis. But for the higher efficiency, it’s better to hire scope-focused specialists even if that will open additional vacancies.

Where to look for machine learning engineers

The hiring approach remains similar: alert and search. Notify that there’s an opening in your team using your company’s website, LinkedIn profile, Twitter, Facebook, etc. as well as the most popular and local hiring platforms like Indeed, Glassdoor, Upwork. You can also try ML-oriented communities on Stackoverflow and GitHub.

Explore the following machine learning engineer requirements that come up in the majority of the position postings to find out what current market needs are and define your own.

Techniques: supervised/unsupervised ML, deep learning, analysis of time series, etc.; Programming Languages: Python, C/C++, Java, Javascript, Julia, Scala, R; Frameworks: TensorFlow, PyTorch, Scikit-learn, Theano, etc.; Tools/OS: Linux, R, SAS, Stata, MATLAB, distribution systems like Hadoop, etc.

Degree: Masters or higher in computer science, statistics, maths or engineering (optional). Experience with: deep neural networks, exploratory data analysis, model building, big data. etc. Skills: analytical, mathematical, etc.

Currently, only for the US, there are more than 17,000 jobs on Indeed are listed with employers like Apple, American Express, Electronic Arts, Spotify, and Twitter. So to attract specifically qualified talents your company job post should be no less detailed and clear about the employment and project specifics and, of course, benefits.

In competition with big players, it’s important to find the balance between project needs and expectations of the executives who can fulfill them. For the SME sector, it means to be more flexible in demands that will allow discovering talents with potential.

The majority of hiring platforms and social networks allow sharing resumes so the area of search in general matches the alerted one. But to the list should be added a machine learning development company selection that includes providers of outsourcing and outstaffing services.

If the vacancy is appeared due to the needs of process optimization in marketing, strategic planning, etc. sometimes it’s better to opt for one of the ready machine learning solutions the company workflow can benefit from.

Some of them come with a subscription basis and can be customized to the given work environment. This way there’s no need in in-house hiring since technical support is usually added to the pricing.

How to hire a machine learning engineer

Once a certain selection of candidates has formed the next issue to resolve is what interview should consist of. Depending on requirements to programming skills and project specifics some initial test assignments can be arranged.

But still, the main part remains a survey that will also help to define soft skills availability like self-consistency, organization, and creativity that are vital for the position.

Explore the subjects of machine learning engineer questions to ask to learn as much as possible about the conformity of an interviewing person to the company ecosystem and particular project needs.

Experience acknowledgment

Ask about what projects the technical skills stated in the resume were used for. Focus on the languages and machine learning techniques mentioned in the job description. Find out the reasoning basis of the applied techniques considering various business aspects including domain. If there’s no correlated background ascertain that a candidate vision of project implementation in the new scope is appropriate.

Look not just into execution but also results. Their success is especially vital for custom projects that have been implemented to solve a particular company problem. If they aren’t available or failed, ask an applicant for a mistake analysis on the concept and technical sides.

Knowledge basis

If the person is applying for an MLE position it doesn't imply even the required level of knowing basics. Although it’s a much more practical vacancy than a data scientist considering its essence only their remembering and full understanding can give the ability to come up with non-traditional approaches that can be vital especially for startups.

The questions should concern not only techniques, stacks or frameworks but also maths (for instance, from the area of linear algebra), statistics (linear regression, Gaussian processes, etc.), probability and data science to ensure that an applicant’s practical skills have a strong theoretical foundation.

Attainment borders

Find out what other instruments are in an engineer’s toolkit and how often and what he or she does to upgrade it. This will define the room for the possible growth within the company and overall as well as show the level of professionalism and attitude towards the position.

Ask about the evaluation of the projects candidates have been working on concerning complexity, responsibility, and interest on their side. Propose to talk about the most fascinating one to estimate the level of enthusiasm and creativity towards the professional scope.

Problem-solving

Questions should concern both probable and actual issues that a former employee of a machine learning company or an enterprise of other scope helped to resolve. Evaluate the reaction, approach and solution to define the level of decision-making skills in the short-term conditions.

Make sure that the list of problems makes the security concerning ones to know how serious this business aspect is taken by a candidate. Test developers prioritizing agility as well by asking to state the reasoned estimation of the issue significance and urgency.

Cooperation ability

Working with data analysis implies the teamwork necessity within engineers and data scientists as well as developed connections between involved departments. For the workflow efficiency, it demands from a candidate certain communication and management skills that should include the ability to track performance and receive/assign tasks with the proposed software.

The clarity to the subject can bring experience gained during previous employment as well as hypothetical questions that will help to identify a candidate as a team player. Some data-driven processes are vital for business operation and their appropriate maintenance can be done only with effective cooperation.

Scope problematic

The survey should concern either AI or a company operational field. Find out the familiarity with methodology and standards of data analysis with machine learning including ethical. Ensure the understanding of the policy of data privacy and its maintaining in the given environment.

An applicant should also have a vision of the domain the company is working in - its importance, connections and internal specifics. And understandably be aware of the company status on the market: its background, operational model, etc. and prove his or her ability to perform on the established level.

What countries to look for machine learning engineers

Over the years Eastern Europe companies have positioned themselves as strong players on the IT market, especially in the outsourcing segment. It was achieved thanks to the shown level of developers’ expertise working on the projects of different nature and complexity worldwide.

One of the leading positions in the region is occupied by Ukraine. It increases in the number of AI service providers according to Clutch not just EE countries but also the UK and Germany making it an optional area for talent acquisition whether for in-house or remote employment.

On machine learning are focused more than 25 percent of AI development companies in Ukraine that makes it a priority direction of the further field progress within the country. Springs is one of the regional representatives which team proficiency and performance cost-efficiency you can ensure to complete your picture of the local market for your staffing needs.

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