There is a very high demand for AI & ML professionals who are qualified enough to do state-of-the-art research and engineering. At the same time, the supply of specialized AI talent is scarce – though the situation is gradually improving thanks to the new Master’s and Ph.D. programs in data science and machine learning that have been launched all over the world in the last few years.
Still, hiring a good ML engineer remains a challenging task for recruiters – not only because of the scarcity of AI talent but also due to a lack of relevant experience among recruiting specialists. Artificial Intelligence remains a new and obscure field for most recruiters.
In this article, we share with you our detailed guidelines for recruiting AI & ML professionals, including the skills to look for, the recruiting strategies to apply depending on the situation, and the advantages you can show off to attract the top talent. We also share some tips for retaining the best ML specialists. But first, make sure you’re not doing these seven things to scare off the AI talent you’re trying to hire.
What Qualities Should You Look For?
If you hope to use your experience in recruiting for traditional software development roles when hiring for AI & ML positions, you may go the wrong way. Despite the apparent similarity between these roles, the skills for successful careers in traditional software development and machine learning differ quite a lot.
While software developers usually work on structured tasks with well-defined deadlines and releases, machine learning experts need to deal with higher uncertainty reflected in lots of exploratory work, experiments, and less clear timelines. Moreover, ML projects require ongoing support and refinement, which doesn’t allow ML engineers to simply move to another project (as software developers usually do).
So, what kind of skills should you look for when hiring for AI & ML roles?
A background in mathematics and statistics is important. Development and training of machine learning models usually require more advanced mathematical intuition than traditional software engineering. To understand which algorithms are better suited for a particular business problem, how to improve the performance of the ML models, and how to interpret the results, ML engineers need to have a good understanding of the mathematics behind these ML algorithms.
In machine learning, learning is crucial not only for machines but also for the people teaching these machines. Rapid learning skills are among the key requirements for ML researchers and engineers as new algorithms and approaches appear at a very rapid rate in the fast-evolving AI area. And to withstand the competition, it is critical to keep up with the latest research breakthroughs.
Creativity is another important quality to look for when hiring for AI & ML roles. The area is relatively new and full of challenges requiring new perspectives and solutions. Your ML engineers should be able to come up with novel ideas for solving the problems that continuously arise.
Also, try to find curious people who would be eager to make sense of abstract information to find a solution to your business problem. Curiosity will also push these people to continual learning and trying new strategies and approaches.
As well as general curiosity, your ML engineers need to have a passion for your specific business and associated business problems. Such a passion will significantly increase their internal motivation, leading to more successful ML projects. If you work in the e-commerce fashion industry, you need to find people who would be really excited by the unique problems you face. Prioritize candidates who have done research or projects related to your industry and business problems.
Finally, AI & ML professionals should have the perseverance to work on long-lasting and challenging projects. They should be ready to spend months experimenting with different ML algorithms before they discover a well-performing solution. Moreover, ML projects are usually never-ending and require constant support and revision.
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Recruiting Strategies For Levels And Experience
Your approach to hiring AI & ML professionals will depend on the level and experience of the professionals you’re looking for. Let’s see what kind of recruiting strategies are best to apply when hiring junior versus senior ML engineers.
Attracting young AI talent
General job boards can be effective in attracting applications. However, they often result in numerous applications from underqualified candidates. The much more efficient approach is to use specialized AI job boards like the ones from Kaggle, Stack Overflow, and TOPBOTS. These job boards usually offer higher-quality candidates who are already part of the AI & ML community.
University partnerships are a powerful tool for recruiting junior staff. They allow companies to identify top young talents and invite them for internships to test their skills with real-world job tasks. This scenario is also beneficial for students who move straight from academia to industry and get experience with real ML projects.
Another good option for attracting junior engineers and data scientists is through organizing competitions or hosting hackathons. For example, you may share part of your data and ask the participants of the competition to suggest the best ML model for predicting customer churn or providing personalized recommendations to customers. The winners of such competitions might be good candidates for internships in your company.
Attracting experienced engineers
Although the specialized AI job boards should be also exploited when hiring experienced ML engineers, this might be not sufficient to hire a really high-profile specialist. In such a case you may need assistance from specialized AI recruiters, who have an extensive network of connections in the AI area and can better identify the candidates to work with your specific business problems.
Top tech companies and small startups recognize AI conferences and meetups as a powerful tool for AI talent acquisition and choose to sponsor or at least participate regularly in different AI conferences. Academic and business conferences and meetups will provide you with a great opportunity to build your own network of AI & ML professionals. Such conferences give you the opportunity to meet individual researchers and engineers, understand the problems they are working on and identify the candidates that can be a perfect fit for your particular business needs.
Also, it can be a good idea to exploit the network of your existing ML team. They are very likely to know ML engineers who work on similar projects for other companies and are good experts in this area.
Retraining existing (software) engineers
Rather than going on the hunt for new talent, many companies choose to retrain current software engineers. Your current engineers may lack expertise in machine learning but they are loyal to your company and – what’s even more important – they have a good understanding of your business. The AI & ML skills they lack may be acquired through online education platforms like Coursera and Udacity, corporate training, or bringing in external trainers.
What Could Be Your Advantages Beyond Salary?
The compensation level is an important factor for ML engineers, like for any other profession. However, you have opportunities to attract good candidates even if you cannot compete with top tech companies in terms of salary.
Let’s see what your advantages can be in the competitive AI job market.
As we have already mentioned, good ML engineers are curious and passionate about their work. Thus, they can be easily attracted by interesting and diverse business problems to solve. Machine Learning engineers working for big companies, like Google or Facebook, are very likely to end up in a very specialized team running a narrow ML project (e.g., image classification in Google Photos or recognition of offensive language on Facebook). In contrast, small companies may offer much more varied ML projects – one day you may be working on a recommender system and another day you can be moved to the team developing a chatbot for customer service.
In addition to challenging and diverse business problems, ML professionals also love data. Thus, you may attract skilled and engaged professionals by demonstrating the availability and quality of data available at your company. Real and well-structured datasets going back for many years can be quite a powerful magnet for ML engineers.
Also, top-level professionals like to work with other top-level professionals. This holds for any area, and particularly for artificial intelligence. You may attract goodML engineers by demonstrating the professionalism and maturity of your existing ML team. Do they apply cutting-edge approaches? Are their research projects presented at top AI conferences? If yes, you have a very good chance of supporting your ML team with a few other capableML experts.
Finally, your candidates for ML roles want to see that their work has a meaningful impact. The advantage of small companies is that the path from idea to model deployment is usually quite short, allowing the ML engineers to see the impact of their work faster. At the same time, ML projects at large companies may affect millions of customers, which can be also very motivational for your job candidates.
How To Retain AI Talent
Once you’ve gathered a strong team of AI & ML professionals, another problem arises: how to keep the AI talent you’ve attracted. Here are a few tips:
- Ensure that your ML team has support from the top management. Regular communication and mutual understanding of expectations are crucial here.
- Support a culture where creative ideas and intellectual discussions are welcome. AI is an area where openness to novel approaches and solutions is crucial for business success.
- Encourage continual learning and attendance at professional conferences and meetups.
- Make sure your ML team has hardware appropriate for the business problems they are working on.
- Consider offering flexible working hours.
Hiring For AI & ML Positions?
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