By Eric Frisch, 10x Management Content Manager
AI and Machine Learning engineers are fast-emerging as the most sought-after positions in the tech industry. Why? AI engineers are building smart solutions for companies that provide long-term advantages to any company’s infrastructure, processes, and logistics. Business leaders will pay handsomely for that sort of competitive advantage. AI makes a company smarter and more adaptable to industry changes by analyzing data trends and using that information to stay ahead of the competition. So the question is, how can your company attract top AI and Machine Learning engineers?
Competition for talent is stiff, and companies like Google, Amazon, Facebook, and Microsoft have so much money that they can monopolize and retain a lot of the best AI minds in the world. It’s not uncommon for those companies to offer million dollar bonuses to retain top-level AI and Machine Learning engineers. Believe the hype, the Great AI Recruitment War is real.
Last year, Amazon spent $227 million on recruiting AI talent with 1,178 job postings seeking AI and Machine Learning engineers. Google was up there too, spending $130 million on recruiting AI and Machine Learning engineers. Apple recently published its first paper on AI, and a new nonprofit group called the Partnership on AI to Benefit People and Society, founded by a group of leading companies including IBM, Amazon, Google and Microsoft is emerging to shape AI policies. The tech giants clearly have a lot at stake in the AI game.
If you’re a startup or an emerging company not quite on the Facebook-Amazon-Google spectrum, looking at these stats can be intimidating. If the tech giants are spending hundreds of millions of dollars on AI and Machine Learning engineers, how can you compete? The truth is, if you’re set on hiring a full-time AI engineer, you’ll face daunting obstacles in your selection process because of that competition. Full-time AI engineers are likely starting their own companies or taking big money with the tech giants. But it’s still possible to hire great AI and Machine Learning engineers on a consultant basis to supplement full-time tech talent.
How can you find the right AI and Machine Learning engineers? First, it’s important to know what you’re looking for.
AI has a lot of different names: Artificial Intelligence, Machine Learning, Data Science, Big Data, Neural Networks, Deep Learning, Natural Language Processing, the list goes on. Here’s a quick rundown – as told by 10x AI Engineers Sam Brotherton and Zack Dvey-Aharon – of what each of these terms mean.
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Artificial Intelligence (AI):
AI is an umbrella term and covers all computerized/algorithmic ways to perform tasks, learn data and react better to it on assignments that might otherwise require human intelligence.
Machine Learning:
Machine Learning is a sub-domain of AI. Machine learning features self-learning mechanisms that become smarter as they have more data.
Data Science:
Data science is the most general term for data analysis. Data can be analyzed manually without any algorithms or learning mechanisms, which means in certain circumstances, it’s not AI at all.
Big Data:
Big Data refers to the collection of a large volume of data on a day-to-day basis that can be used for analysis and discovery. Big Data can be both structured and unstructured.
Neural Networks:
Neural Networks are computer systems modeled on the human brain. Neural networks allow computers to learn and progressively improve based on data without hard-coding changes. Computer systems are given examples and evolve their own analysis method from the material they’re provided.
Deep Learning:
Deep Learning is closely related to Neural Networks and also modeled on the human brain.
Natural Language Processing:
Natural Language Processing is a branch of Artificial Intelligence that facilitates the interaction between computer and human languages. Natural language processing requires a massive amount of training data in order for computers to learn the nuances in human language. If you’ve ever used Amazon’s Alexa, that’s a perfect example of Natural Language Processing in action!
What do all of these terms have in common? For one, they’re all so commonly used that they’ve essentially become synonymous with Artificial Intelligence. Second, they all require a massive amount of training data in order to learn and improve.
Access to training data is another area where tech giants like Google, Amazon, and Microsoft have a huge advantage over smaller companies. Training data is incredibly expensive to obtain, and the tech giants have a massive head start on the rest of the world; they’ve been collecting it for a long time. A typical startup or smaller-size company doesn’t have access to mountains of data. Companies can try and compensate by bootstrapping data and integrating third party data sources like Wikipedia and Yelp datasets, but it’s never as good as customized, real data.
What to Look For When Hiring AI and Machine Learning Engineers
Now that you’re familiar with many of the terms for AI, it’s important to understand what characteristics you’re looking for in your next AI and Machine Learning engineers. At 10x Management, we take great pride in the technical achievements of our clients, and we thoroughly vet our talent to make sure they’re at the top of their engineering class. But technical achievements are only one part of the puzzle (for more on this and how to manage 10x-level talent, check out our upcoming book, Game Changer). Finding and matching great talent is our expertise, and here’s a list of some of the qualities we look for:
Technical Skills
As stated above, the ability to code/program/engineer is obviously an important skill for any engineer to possess. Technical prowess is at the top of the list when it comes to what to look for when hiring AI and Machine Learning engineers. There are a lot of ways to test for technical prowess. Take a look at an engineer’s past achievements; this will tell you a lot. If you’re dealing with an inexperienced engineer, give them a coding test to make sure their skills are up to par. Find other engineers, programmers, and coders to vet your talent if you’re unsure, or find an AI engineer through an agency.
Communication
Communication abilities are right up there with technical skills in terms of importance to a project. To quote George Bernard Shaw, “the single biggest problem with communication is the illusion that it has taken place.” At 10x Management, we literally have an entire manual dedicated to communicating with customers for our talent. We recommend that AI and Machine Learning engineers agree on a communications framework with a company before the project begins. If you’re worried about communication, find an engineer that is clear and concise and puts those worries to rest. If confusion sets in, reset your communication parameters or find a new engineer.
Curiosity and Creativity
The project that you have in mind for your AI and Machine Learning engineers should spark their imagination and ignite their creativity. They should ask you questions and you should feel inspired by their passion for the project. AI and Machine Learning Engineers should go out of their way to understand why they are building or improving something so they can make recommendations that are profoundly valuable to the project.
Ability to Adapt to New Technologies
There’s a famous quote, “change is the only constant in life,” and that has never been truer than today in technology. AI and Machine Learning are constantly-evolving fields with new sectors and technologies being added every day. Your AI and Machine Learning engineers should be able to stay current with shifts in technology and adapt to industry trends to implement solutions that work for today.
Set Detailed and Deliverable Goals
Great AI and Machine Learning engineers will break down tasks into smaller, measurable, and deliverable goals as benchmarks for progress. Build a detailed plan with your AI and Machine Learning engineers that outlines the exact steps to take to build something meaningful. Intermittent goals are critical to displaying progress in action. Don’t evaluate your project based on one final outcome; this is a recipe for disaster.
Cultural Fit
If corporate culture is important to you, then your AI and Machine Learning engineers should respect that culture. Sometimes, respecting a corporate environment can be as important as the work itself. Your AI and Machine Learning engineers should be professional and respectful, assuming those qualities matter to you.
Preparation
Great AI and Machine Learning engineers will tell a customer before a project begins what they need to get started on day one. There should be no time wasted asking questions and figuring basic things out on day one.
Remember, technical chops are only one part of the puzzle. Great engineers that can’t communicate their solutions, have a bad attitude, or cause cultural rifts at your company are not worth their technical ability.
Where to Find AI and Machine Learning Engineers
Knowing what to look for in an AI or Machine Learning engineer won’t get you too far if you don’t know where to look.
As AI expert Zack Dvey-Aharon says, “it’s important to do research on AI engineers that have been contracted by competitors or other companies in the field. My firm has delivered more than 40 AI projects to clients, and in each area, my past AI engineer experience with similar problems turned out to be a crucial factor.”
Companies that source AI engineers and development talent must understand two key parameters:
- How strong and experienced is the engineer? This question comes back to technical skills and communication abilities.
- How easily can their work be integrated with the company, its IT team and the general “data DNA” of the firm? This question comes back to cultural fit.
Here’s a list of places where you might find AI and Machine Learning engineers:
- Open marketplaces require more stringent due diligence and carry a larger degree of risk (think Upwork).
- Curated marketplaces remove some guesswork because they usually sift out the lousiest parts of the marketplaces, while still allowing the customer to make the right match.
- Dev Shops can be great. The one flaw in their model is that the less the shops pay their talent, the higher their margins. That’s why you’ll find a fair number of unhappy employees in their midst. And there’s nothing worse than being billed out at $250/hour and going home with just $60/hour. Resentment can build fast.
- Agencies vary. Some act more like personal shoppers or matchmakers and work hard to understand your needs and identify the right fit for the role (think 10x Management). The good ones can save you tons of time.
How to Land the AI & Machine Learning Engineers You Want
AI is still a new area of tech that requires a unique skill set. From our experience, AI and Machine Learning engineers crave freedom to build creative solutions to problems.
One of 10x’s AI experts, Sam Brotherton, explains why he left Google to pursue freelance AI work:
“Personally, I transitioned to remote work on a consultant basis because I enjoy working with different clients, traveling, figuring out what my clients need and doing it more independently. At Google, I was an individual contributor on some exciting projects, but I didn’t have the independence I sought to make a difference. I live in Salt Lake City and want to be close to my family, but I also enjoy working on smaller teams with different clients.”
It’s likely that each AI and Machine Learning engineer you bring onboard will have their own unique style, and it’s up to you to adapt your approach to get the most out of your engineers. Have an open discussion about creating the best work environment possible. Take the time to understand what’s important to your AI and Machine Learning engineers, and they will do the same for you.
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