How to Find and Hire Your First AI Engineer
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The myth of AI tech skill shortage
The biggest tech companies — Google, Baidu, Facebook, Apple, Amazon, Salesforce, and Uber — have been hiring entire AI research departments and buying AI startups for astounding valuations.
These behemoths are slugging it out to make fundamental AI advances. Baidu can make millions if its speech recognition algorithms are 1% better than Google’s Speech API.
Your company is not Baidu. Your competitive advantage comes from applying AI to your data and innovating on your business model. Most of your technical headaches will be in ingesting, organizing, processing and storing data — not tuning neural networks.
Qualified AI engineers are in demand — but the shortage is no bigger than any other programming skill
Finding great programmers for anything is hard. It has always been hard. There are fewer AI engineers but also less competition for talent.
How to you hire your first AI Engineer
You need a good programmer.
You don’t need an AI researcher…
You need a good programmer.
You don’t need a data science expert…
You need a good programmer.
You don’t need a PhD…
You need a good programmer.
Got it?
Of course you may find an AI researcher, data scientist, or PhD who is also a good programmer — if so, congratulations. But your first criteria should be finding a programmer with a proven history of shipping products.
90% of practical AI work is writing software to ingest, organize, transform, evaluate, process, and store data. This is tedious, boring, and (often) thankless work.
10% of the work is “AI programming”: designing networks, tuning parameters, selecting activation functions. This work is so fun that programmers will enter Kaggle competitions on evenings and weekends.
You need server-side software and data operations skills. You don’t need HTML, javascript, CSS, or IOS skills.
Jeremy Howard’s fast.ai course has 4 simple criteria for succeeding in his course — your first AI hire needs these same characteristics:
Recruiting strategies
Finding the right AI Engineer takes the same tenacity and time as hiring any for any technical position. Fortunately many A+ programmers are picking up AI skills because it is one of the most interesting new tech domains. The right candidates will be looking for opportunities to apply what they are learning in online courses and side projects.
You absolutely positively must, must, must find a programmer who has demonstrated initiative to learn AI on her own.
Does your prospective candidate ask for training? If so, run.
A few recruiting sources:
- Start by asking if any programmers in your organization are familiar with popular AI books and courses. Or those who enter Kaggle competitions.
- If you don’t find an internal candidate go to a local machine learning or data science Meetup. Sponsor or host an event or just ask the organizer for permission to post a job.
- Search Kaggle competitions.
- Search Medium tags for topics like Artificial Intelligence and look for articles by programmers working on side projects.
- Look at the forums in online courses like forums.fast.ai.
If you’re not a programmer yourself you’ll need one to interview candidates and ask about their work.
AI Engineer Interview and candidate screening questions
- Why are you interested in AI? How are you learning about it?
- Have you completed any courses? Can you share any models you’ve created? Do you have any notebooks on GitHub you can share?
- Have you entered any Kaggle competitions? If so, what was it and what was your experience?
- Would you say your skills are stronger in (1) data science, or (2) programming? What is your plan for getting more proficient?
- Let’s pretend you are in charge of setting up our AI development infrastructure. How would you do it? What tools do you need?
- Suppose you are paired with a product manager whose job is to gather business requirements and training data for you. She asks you, “what do you need?” — how would you respond? Feel free to use a specific project in computer vision, collaborative filtering, or NLP as an example.
- Have you read any interesting AI papers lately? If so, what?
