how to hire a machine learning engineer.

how to hire a machine learning engineer.

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If your company is in the technologies industry (or dabbling therein), it's a safe bet you're in the market for a machine learning engineer. These engineers are responsible for programming machines to learn from experiences and behave accordingly, and in doing so, they run everything from conducting experiments to deploying and optimizing solutions. It all sounds a bit futuristic, right? Well it is and it isn't.

The notion and field of machine learning has actually been around for decades — remember Alan Turing? — but has enjoyed a recent rise thanks to the resurgence of artificial neural networks in practical applications. The end result being machine learning engineers are now one of the fastest-growing and most in-demand jobs in engineering. In fact, Burning Glass data shows that there have been more than 12,000 job postings for machine learning engineers in the past year alone — and demand is expected to continue increasing 12x year over year.

So how can your company hire a top-quality machine learning engineer in such a competitive market? To start turning this challenge into an opportunity, we've outlined some steps to take when looking for your next great hire:

Ready to dig in and get started? Keep reading for tips on how to address each of these steps.

1. speed up your hiring process

What does low unemployment plus a projected boom in the machine learning market and already high demand for machine learning engineers equal? A fiercely competitive talent hunt. This means that employers looking for engineers should do all they can to speed up their hiring process.

Of course, we're not encouraging being hasty or rash — but rather, smart and streamlined. Use the diagram below to determine where your process might benefit from an efficiency tune-up.

2. develop a competitive compensation package

It's no secret that perks and benefits play heavily into tech talent's choice of employer. In fact, for some they're more of a deciding factor than salary! So regardless of your salary offering (more on that in a moment), be sure to evaluate your current perks and benefits to ensure they'll meet the standard of today's tech world. For example, onsite health and wellness amenities and flexible work schedules can go a long way, even if you're not able to offer the highest salary among your competitors.

Speaking of salaries, a machine learning engineer is one of the highest earning positions in the technologies industry. But don't let that scare you — they're worth every penny. To kick off your research, make use of Randstad's 2019 Salary Guide, which provides national salary averages for this and other important positions. A competitive salary will also vary greatly by region, too. So be sure to use our salary calculator for the most up-to-date, location-specific salary data for machine learning engineers, as well as other key positions in the technologies industry.

3. identify the top skills for machine learning engineers

Before you tackle the job description, it's important to have a good understanding of what skills and experience are necessary for a machine learning engineer to be successful at your company. Get together with key leaders on your engineering team and the hiring managers do discuss which skills are most needed (your "must-haves"), and which would be a bonus (your "nice-to-haves").

But be careful not to let your bonus skills creep into required skills. While everyone wants to find the most ideal candidate possible — and you should strive for that! — prematurely cutting a candidate loose because they don't possess all your bonus skills could mean missing out on a potential best-fit hire. Machine learning engineers are always hungry to learn, so you can count on just about any candidate being able to pick up any bonus skills you'll need down the line.

Here are the top skills for machine learning engineers, as requested by employers across the nation in the past year:

machine learning

Python

data science

artificial intelligence

Java

TensorFlow

deep learning

4. write an eye-catching job description

Your job description should catch candidates' eyes and their interests. Keep the following pointers in mind to ensure your description will appeal to the best applicants out there.

don't write like a robot
Although a machine learning engineer would probably appreciate the power of a robot more than most people, you still want to be sure it's obvious there's a real, live human trying to solicit their interest.

To ensure you're showing your human side, package all aspects of the description in personable language that's reflective of your company's voice and brand. Moreover, because so much of machine learning is research and invention, prospects will want to know how much of the job relies on (or is restricted by) existing research. Top candidates in this field will have questions like:

  • Will I have a part in furthering the company's research and invention efforts?
  • Will I have freedom to venture out, or will I be expected to work within established research?

Anticipating — and answering — these sorts of questions in the job description signals to machine learning engineers that they'd be working with people who understand exactly what they do.

show how the job is meaningful
Machine learning requires creativity, experimentation and tenacity. And not all of these traits can be entirely taught; some are simply possessed — which is why machine learning talent is so valuable.

Candidates want to know you understand what they bring to the table, so make sure your job description highlights this value and explicitly states the ways in which you view this role as meaningful to the work your company does.

Also, put a spotlight on the work your company does — both the larger purpose of the work as well as any social impact initiatives. Today's workforce wants a sense of purpose in their work, so knowing their contributions will have lasting positive impact can be a great influencing factor.

machine learning engineers want to know you understand their value — so highlight the ways your company sees their work as meaningful.

make it easy to skim
Rambling sentences and dense paragraphs make job descriptions a tedious read. The goal is to organize all of the information into a skimmable format so readers can easily take in the details as they scroll. Writing point-first sentences and short paragraphs make a big difference, as well as using bullet points for listable items, such as perks and benefits.

With demand as high as it is, you'll want to learn all you can about how to stand out from your talent competitors. Read up on more insights in our guide to writing highly effective job descriptions.

5. brush up on the latest industry trends

In our fast-paced digital world, future-proofing is a necessity. You're not just hiring for today's needs after all, you want to be proactive in defining what you'll need for tomorrow as well. To be an industry leader, companies must have the right talent — and to get the right talent, companies must have a long-term hiring strategy that's tailored to industry landscape and trends.

Take a look at these trends to know what's currently impacting both the technologies industry and the future of hiring for machine learning engineers:

Learn even more about the technologies industry and the shifts and trajectories shaping the hiring landscape in our 2019 trends guide.

California has four times the number of machine learning jobs than any other state.

6. choose your channels to find candidate

Your hiring process is in good order and you've put together a great compensation package. Top skills have been identified, a crack job description is written and you've brushed up on relevant industry trends. Now what? Now it's time to put your posting out there.

But where?

Company websites are a good place to start — especially if you've got a robust careers page. Leveraging employee referrals and LinkedIn networks can also yield good results. Job boards are a popular option for finding talent, too. But, if you're serious about finding top talent fast, you'll likely find yourself waiting longer than you'd like for the right applicant to come along.

If you're looking to expand your efforts, consider partnering with a professional staffing firm, which can alleviate many of these worries and risks. When you have industry experts and a prescreened and vetted talent pool at your fingertips, you can feel confident that you're doing all you can to mitigate a bad hire and maximize efficiency. 

Want to learn more? Reach out to the experts at Randstad Technologies today to discuss your machine learning engineer needs. Or you can browse our network of quality engineering candidates right now in our Find Employees portal.

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how to hire a machine learning engineer.

Posted by Michael Cirigliano on Sep 27, 2019 1:28:19 PM

If your company is in the technologies industry (or dabbling therein), it's a safe bet you're in the market for a machine learning engineer. These engineers are responsible for programming machines to learn from experiences and behave accordingly, and in doing so, they run everything from conducting experiments to deploying and optimizing solutions. It all sounds a bit futuristic, right? Well it is and it isn't.

The notion and field of machine learning has actually been around for decades — remember Alan Turing? — but has enjoyed a recent rise thanks to the resurgence of artificial neural networks in practical applications. The end result being machine learning engineers are now one of the fastest-growing and most in-demand jobs in engineering. In fact, Burning Glass data shows that there have been more than 12,000 job postings for machine learning engineers in the past year alone — and demand is expected to continue increasing 12x year over year.

So how can your company hire a top-quality machine learning engineer in such a competitive market? To start turning this challenge into an opportunity, we've outlined some steps to take when looking for your next great hire:

Ready to dig in and get started? Keep reading for tips on how to address each of these steps.

1. speed up your hiring process

What does low unemployment plus a projected boom in the machine learning market and already high demand for machine learning engineers equal? A fiercely competitive talent hunt. This means that employers looking for engineers should do all they can to speed up their hiring process.

Of course, we're not encouraging being hasty or rash — but rather, smart and streamlined. Use the diagram below to determine where your process might benefit from an efficiency tune-up.

2. develop a competitive compensation package

It's no secret that perks and benefits play heavily into tech talent's choice of employer. In fact, for some they're more of a deciding factor than salary! So regardless of your salary offering (more on that in a moment), be sure to evaluate your current perks and benefits to ensure they'll meet the standard of today's tech world. For example, onsite health and wellness amenities and flexible work schedules can go a long way, even if you're not able to offer the highest salary among your competitors.

Speaking of salaries, a machine learning engineer is one of the highest earning positions in the technologies industry. But don't let that scare you — they're worth every penny. To kick off your research, make use of Randstad's 2019 Salary Guide, which provides national salary averages for this and other important positions. A competitive salary will also vary greatly by region, too. So be sure to use our salary calculator for the most up-to-date, location-specific salary data for machine learning engineers, as well as other key positions in the technologies industry.

3. identify the top skills for machine learning engineers

Before you tackle the job description, it's important to have a good understanding of what skills and experience are necessary for a machine learning engineer to be successful at your company. Get together with key leaders on your engineering team and the hiring managers do discuss which skills are most needed (your "must-haves"), and which would be a bonus (your "nice-to-haves").

But be careful not to let your bonus skills creep into required skills. While everyone wants to find the most ideal candidate possible — and you should strive for that! — prematurely cutting a candidate loose because they don't possess all your bonus skills could mean missing out on a potential best-fit hire. Machine learning engineers are always hungry to learn, so you can count on just about any candidate being able to pick up any bonus skills you'll need down the line.

Here are the top skills for machine learning engineers, as requested by employers across the nation in the past year:

machine learning

Python

data science

artificial intelligence

Java

TensorFlow

deep learning

4. write an eye-catching job description

Your job description should catch candidates' eyes and their interests. Keep the following pointers in mind to ensure your description will appeal to the best applicants out there.

don't write like a robot
Although a machine learning engineer would probably appreciate the power of a robot more than most people, you still want to be sure it's obvious there's a real, live human trying to solicit their interest.

To ensure you're showing your human side, package all aspects of the description in personable language that's reflective of your company's voice and brand. Moreover, because so much of machine learning is research and invention, prospects will want to know how much of the job relies on (or is restricted by) existing research. Top candidates in this field will have questions like:

  • Will I have a part in furthering the company's research and invention efforts?
  • Will I have freedom to venture out, or will I be expected to work within established research?

Anticipating — and answering — these sorts of questions in the job description signals to machine learning engineers that they'd be working with people who understand exactly what they do.

show how the job is meaningful
Machine learning requires creativity, experimentation and tenacity. And not all of these traits can be entirely taught; some are simply possessed — which is why machine learning talent is so valuable.

Candidates want to know you understand what they bring to the table, so make sure your job description highlights this value and explicitly states the ways in which you view this role as meaningful to the work your company does.

Also, put a spotlight on the work your company does — both the larger purpose of the work as well as any social impact initiatives. Today's workforce wants a sense of purpose in their work, so knowing their contributions will have lasting positive impact can be a great influencing factor.

machine learning engineers want to know you understand their value — so highlight the ways your company sees their work as meaningful.

make it easy to skim
Rambling sentences and dense paragraphs make job descriptions a tedious read. The goal is to organize all of the information into a skimmable format so readers can easily take in the details as they scroll. Writing point-first sentences and short paragraphs make a big difference, as well as using bullet points for listable items, such as perks and benefits.

With demand as high as it is, you'll want to learn all you can about how to stand out from your talent competitors. Read up on more insights in our guide to writing highly effective job descriptions.

5. brush up on the latest industry trends

In our fast-paced digital world, future-proofing is a necessity. You're not just hiring for today's needs after all, you want to be proactive in defining what you'll need for tomorrow as well. To be an industry leader, companies must have the right talent — and to get the right talent, companies must have a long-term hiring strategy that's tailored to industry landscape and trends.

Take a look at these trends to know what's currently impacting both the technologies industry and the future of hiring for machine learning engineers:

Learn even more about the technologies industry and the shifts and trajectories shaping the hiring landscape in our 2019 trends guide.

California has four times the number of machine learning jobs than any other state.

6. choose your channels to find candidate

Your hiring process is in good order and you've put together a great compensation package. Top skills have been identified, a crack job description is written and you've brushed up on relevant industry trends. Now what? Now it's time to put your posting out there.

But where?

Company websites are a good place to start — especially if you've got a robust careers page. Leveraging employee referrals and LinkedIn networks can also yield good results. Job boards are a popular option for finding talent, too. But, if you're serious about finding top talent fast, you'll likely find yourself waiting longer than you'd like for the right applicant to come along.

If you're looking to expand your efforts, consider partnering with a professional staffing firm, which can alleviate many of these worries and risks. When you have industry experts and a prescreened and vetted talent pool at your fingertips, you can feel confident that you're doing all you can to mitigate a bad hire and maximize efficiency. 

Want to learn more? Reach out to the experts at Randstad Technologies today to discuss your machine learning engineer needs. Or you can browse our network of quality engineering candidates right now in our Find Employees portal.

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Topics: phase:explore, industry:technologies, topic:problems