Businesses in just about every industry are using machine learning to innovate, boost productivity, and lower costs. It's helping companies improve customer relations, automate key practices, and scale at a rapid rate. Smart business leaders are joining the trend by hiring experience machine learning engineers to lead up their machine learning efforts.
Of course, it isn’t always easy finding a great team of machine learning engineers. It can be challenging to find people with the right experience or even the right set of complementary soft skills needed to seamlessly integrate into your business and work effectively with your current employees.
Assembling the right team takes time, patience, and a clear set of benchmarks. This article will lay out strategies for creating a top-of-the-line machine learning engineering staff. You’ll learn how to locate the best engineers and how to get them to join your team. You’ll also learn how to define their roles and what kinds of salaries you should be paying them.
What Is Machine Learning?
Machine learning is a broad term that refers to automating computer processes and building algorithms that can make decisions on their own. It can be applied to just about every sector in today’s economy, from construction to public transportation.
You've likely interacted with machine learning without being aware. In online retail, for example, machine learning powers chatbots that help customers make decisions about their purchases. Machine learning tools can also help businesses make accurate predictions about consumer tastes and behaviors. Platforms like Spotify and Netflix likewise use machine learning algorithms make predictions about what kind of entertainment you might enjoy.
Machine learning has found application in the industrial sector, too. It's being used to track supplies and make predictions about when and where inventory will be needed. It also helps plan when maintenance and repairs need to be carried out so that businesses don’t experience unplanned downtime.
Related: Generative AI vs. Machine Learning: Key Differences and Uses
What Do Machine Learning Engineers Do?
It may help to think of machine learning engineers as being both data scientists and computer programmers at once. The best machine learning engineers have a bent for statistics and a passion for data. They combine that with technical skills to build machine learning models that can analyze data, identify patterns, and create accurate predictions.
As was mentioned, machine learning engineers create the bots on websites that chat with customers, answer questions, and collect information. They also build machine learning models, which are algorithms designed to look for patterns in data. These models sift through mountains of data to come up with the most important information.
Machine learning engineers' essential duties usually include:
- Planning, implementing, and overseeing the operations of machine learning models
- Carrying out data-based analysis based on the needs of your business
- Fine-tuning existing machine learning algorithms to better suit the changing needs of your business
- Reporting to the stakeholders in your business about the results of their machine learning experiments
- Coordinating with stakeholders to plan and refine your company’s machine learning strategy
In many cases, they act as a bridge between your company’s technical and non-technical staff. They can help assess exactly where machine learning tools might improve your business, make sure those tools are implemented correctly, and maintain clear lines of communication with stakeholders to ensure that the project is accomplishing its goals.
What Are the Key Machine Learning Roles?
Before you begin the process of hiring, you’ll need to have a solid understanding of the different positions on a typical machine learning team. Here are some of the most common machine learning roles:
Data Analysts
Data analysts are at the front lines of machine learning. They take data and mine it for insights into just about anything, from consumer behavior to traffic patterns.
A good data analyst will have experience working with a few different computer languages. They’ll also be familiar with data visualization tools. Most data analysts have a background in statistics, though they may also have a specialty in a particular business niche like marketing or risk analysis.
Data Engineers
This role is primarily geared towards building the infrastructure that allows analysts to do their job. Data engineers handle capturing, storing, and processing data so that it can be accessed and analyzed.
They should be experienced working with tools that manage large volumes of data, like Hadoop or Spark. They should also be familiar with managing data structures and algorithms as well as overseeing ETL projects.
Data Scientists
Data science has some overlap with data analysis since both involve analyzing and interpreting data. However, data scientists also work closely with other members of your business by sharing their data-driven insights. This means that data scientists need to have all the technical skills that data analysts possess — but data scientists also need strong soft skills like verbal and written communication skills.
Data scientists are often responsible for building ML algorithms that can analyze patterns in data. In addition to their other technical skills, data scientists need to know how to use Python and SQL. Like data analysts, they should have a strong foundation in statistics.
Research Scientists
These are responsible for staying current with the very latest developments in machine learning and artificial intelligence so that they can implement new tools whenever they might benefit your business. Research scientists may have more formal education in computer science than other machine learning professionals possess. A good research scientist is also familiar with areas on the cutting edge of machine learning like Computer Vision or Natural Language Processing (NLP).
It’s worth noting that not every business needs to employ a research scientist. If your company is in the tech sector, it’s probably useful to have someone on staff who has their finger on the pulse of latest developments. If your competitors are using the latest technology and leaving you behind, then it might be worth your while to hire a research scientist so that you can catch up with them or even overtake them.
Machine Learning Engineers
Data scientists usually work very closely with machine learning engineers to build and update machine learning models. Machine learning engineers don’t just focus on one model — they're concerned with the overarching machine learning infrastructure and its role in your business.
A good machine learning engineer will have experience using cloud services and deployment tools like Cortex and PastAPI. They'll also be familiar with data orchestration tools like Kubernetes and different deployment strategies.
What Is a Typical Machine Learning Engineer Salary?
Machine learning engineers earn different salaries based on their locations. That’s why many businesses choose to hire remote engineers based abroad.
Machine Learning Engineering Salaries in the United States
The average annual salary of US machine learning engineer is $157,007.
That figure will change depending on your exact location, of course. The average annual salary of a US machine learning engineer living in New York City is $186,862. In Cupertino, CA, the average machine learning engineer can earn $182,962.
Indeed.com also notes that many machine learning engineers based in the United States earn benefits like health insurance, dental and vision insurance, 401K, and gym memberships. Where relevant, machine learning engineers may be eligible for relocation expenses and other perks.
Machine Learning Engineering Salaries in Eastern Europe
Workers in Eastern Europe often earn less than those in the United States or Western Europe. In Romania, for example, the average machine learning engineer earns about RON 79200 or $17,222.
In Poland, the average machine learning engineer earns more than in Romania, but still significantly less than they might in the United States. They typically bring home PLN 136668, or the equivalent of $32,880 U.S. dollars.
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Machine Learning Engineering Salaries in Latin America
Machine learning engineers earn lower salaries in Latin America, largely because the cost of living is much lower than it is in the United States. In Brazil, for example, the average annual cost to hire senior Brazilian machine learning engineers is around $99,692.
In Colombia, the average annual cost to hire senior Colombian machine learning engineer is around $101,264.
And in Chile, the average annual cost to hire a senior Chilean machine learning engineer is approximately $106,960.
Where to Find Machine Learning Engineers
Once you’ve determined the machine learning positions that you need to fill, it’s time to start assembling your team. Here are some of the best ways to find experienced machine learning engineers for your team.
Job Boards
As old fashioned as it may sound, job boards can still be an effective means of recruiting engineers to join your business. Be aware that job boards are probably more geared at recruiting younger, less experienced engineers. If you’re looking for relatively junior staff members to fill entry-level roles like data analyst, then a job board may be a good place for you to start. If, however, you’re looking for more senior staff, you may need to use a different approach.
If you do choose to use job boards, be sure to use specialized boards. Generalist job sites like Idealist or Monster Jobs may attract too many candidates who lack the qualifications that you need. Instead, look for boards that are aimed at skilled workers in the tech sector. You have a better chance to find what you're looking for on sites like AiJobs.net and Data Elixir.
If you’re using job boards, you should also be prepared to spend plenty of time looking at resumes, checking references, and conducting interviews. When you place an ad on a job board, you’ll probably generate a massive response — and that means that you’ll need to spend a lot of time sifting through applications and examining candidates to find that one that’s right for you.
Competitions
Some companies hunt out the best tech talent by holding competitions. Sometimes referred to as hackathons, these competitions are becoming popular with larger employers, especially institutions like universities and government agencies.
Prospective employers hold hackathons that challenge competitors to solve existing problems or propose new approaches to a project. The creator of the best solution may be offered a job. One unexpected benefit of hackathons is that people who participate in such competitions may be more likely to sign up with the company. That’s good news if your company is struggling to attract and retain the best tech talent.
Of course, organizing a hackathon does come with some built-in downsides. Like most hiring processes, it involves a major time investment. You’ll have to dedicate a lot of labor hours to planning, publicizing, and judging the competition.
Outsourcing
Sometimes, companies do well when they outsource projects to a business that specializes in machine learning and artificial intelligence. At its best, this relieves pressure on your business by shifting the project to a different team. Outsourcing can save businesses money since they won’t be paying benefits and other bonuses. Of course, you may also end up paying higher rates to outsource a project depending on the business that you contract with.
Outsourcing tends to work best when you have a well-defined project with clear benchmarks. It also helps if you have staff with the capacity to oversee the work and determine whether the progress is up to expectations.
Outsourcing may not work as well if you want to build a long-term machine learning strategy for your business which would require the ongoing involvement of a trained machine learning engineer. Likewise, outsourcing may not be right for you if you want to be more involved in the day-to-day operations of your machine learning team.
Hiring With Revelo
Revelo is a talent platform for tech talent. They match companies with top machine learning engineers and similar talent in related tech fields who become full-time hires embedded in your business’ teams. Revelo does not supply project work or contractors. The company provides trained employees who onboard with your company and are fully invested in your team.
Revelo conducts a thorough vetting of all of its tech talent — all those who are represented have been checked to make sure that their education, work experience, and backgrounds are all accurate and that they have the skills needed to perform at the highest possible level. You can stipulate exactly what kinds of hard and soft skills you're looking for so that Revelo will be able to match you up with the perfect candidate.
How to Hire Machine Learning Engineers
Getting the right start with the hiring process begins with having effective job descriptions and interview questions. Here are tips to put you on the right track.
What Should a Machine Learning Engineer Job Description Look Like?
Creating a detailed, accurate job posting makes it easier to find the right machine learning engineer for your company. Just as your business is unique, your job description will also be unique. Make sure that your job post paints a clear picture of the position you need to fill. It should include the following key items:
- An overview of the daily, weekly, and monthly duties that a machine learning engineer is expected to perform
- The reporting structure at your company. Will your new machine learning engineer be responsible for supervising a team of junior engineers? Who will they be reporting to?
- Your company’s short and long-term goals, and how you expect a machine learning engineer to contribute to those goals
- Your company’s overall mission and purpose
Interview Questions for Machine Learning Engineers
Interviews are a good way to filter out inappropriate candidates and find the right person for your position. They also give you the opportunity to assess whether a candidate would be a good fit with your team on an interpersonal level.
It’s a good idea to ask questions that can determine whether a candidate has the right skills and experience to work as a machine learning engineer in your company. Sample questions could include:
- How would you implement machine learning strategies at this company?
- How do you stay informed about the latest developments and trends in machine learning technology?
- How do you explain machine learning strategies to people who are not familiar with the technology?
In addition to these questions, it’s a good idea to chat with candidates about their past experience and their goals for the future.
Source and Hire Machine Learning Engineers With Revelo
Machine learning can take your company to the next level by automating processes, generating insights, and helping you to come up with data-driven business decisions. But in order to reap the benefits of machine learning, you need to have the right team of machine learning engineers on your side. In most cases, that means having an in-house machine learning team that can report directly to your stakeholders.
Revelo can help your business find experienced, skilled, and vetted machine learning engineers who are based outside of the United States but are in your own time zone, instead of being ten hours ahead.
We make it easy to hire low-cost, highly skilled machine learning professionals to join your business. We only work with experienced, proven tech specialists who can hit the ground running and start adding value to your business right away.
You can start building your machine learning team risk-free by using Revelo's 14 day free trial. Visit us today to learn more about hiring tech professionals through our platform.
Further Resource: Alternative Software Developer Talent Acquisition
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