If you are building an in-house AI team, whether directly or via recruiters, it will be important to understand the roles you require and how to attract, deploy and maintain talent. Below, we provide a blueprint for structuring, building and retaining your AI team.
Because AI is an emerging field, job titles vary and can be difficult to interpret. Further, people may describe themselves in different ways to market themselves for roles they want. There are at least six core roles in AI. In each, individuals’ capabilities vary across competencies of research, engineering, production and strategy (Fig. 5).
|Head of Data||Medium||Medium||Medium||Medium|
|Head of AI||High||Medium||Medium||Medium|
Source: MMC Ventures
Salary range and job expectations will vary according to an individual’s role (Fig. 6).
The composition of your AI team should depend on the problem being solved, your team’s approach to doing so, and the level of integration required with your development team to support production. Bear in mind, however, the following principles:
“Structure your team to avoid lone workers. AI professionals rely on collaboration.”
|Job title||Salary range (£ thousands)||Job expectations|
|Data Engineer||45 – 90||
|Machine Learning Engineer||60 – 90||
|Data Scientist||45 – 90||
|Machine Learning Researcher||60 – 100+||
|Head of Data||80 – 120||
|Head of AI||80 – 120||
|Chief Scientist||110 – 180+||
Source: MMC Ventures
Unless you are a known company, advertising on your own website is unlikely to be effective. Alternative sources include:
1. Recruiters: Specialist recruiters exist for AI and data science. Poll your network to identify them. Unlike traditional development roles, recruitment agents find it challenging to screen AI candidates due to the research-intensive, data- specific problems they tackle. High quality AI recruiters have extensive networks of candidates, can identify candidates that fit your needs, and can save you more in the cost of your time than they charge in fees (typically 10%-45% of annual salary, plus bonuses).
2. Conferences and Meetups: Conferences and meetups are a powerful vehicle for talent acquisition. Be active in the AI community and grow your network. Every major city has an AI network you can join and there are conferences throughout Europe almost every week of the year. Many conferences offer job boards, in addition to which you can meet individuals at the event and undertake an initial screen. Even if timing does not align between you and potential candidates, making connections is valuable and you will become known as a potential employer.
Consider speaking at events – it’s easy to include a “we’re hiring” closing slide and you may receive extensive interest. Some conferences require sponsorship for a speaking slot. This may be appropriate if you are discussing your general solution in a talk close to a sales pitch. You should never have to pay, however, if you have an advancement that will benefit the community. Submit your paper to an academic conference or one of the many high quality events that avoid sponsored talks and seek excellent speakers and and topics.
Academic conferences – including NeurIPS, ICML, ICLR and AAAI – are busy events at which larger employers are highly active. These conferences are expensive and smaller companies can be overlooked. They are, however, valuable events if you seek exceptional researchers.
Other conferences, including RE•WORK and M3, focus on the intersection of academic and business applications
These can be excellent environments in which to meet individuals who wish to move from academia to industry, as well as being stimulating environments for general conversation and ideation.
Local meetups are even less formal and many offer events specifically for hiring. These are an excellent option if your team has not spoken before or if you wish to receive feedback on your approach. If there is not a meetup near you, start one.
3. Universities: If you are geographically close to a university with a strong AI department, or have alumni connections to one, the university may allow you to post on its job boards. While roles will be focused on students soon to graduate, alumni can also see the university’s digital job boards and your role may be passed around networks
of AI practitioners.
You may also be able to work in partnership with a university by paying for projects. Typically, the head of a laboratory will accept a project for a fixed cost and the project will serve as a task for graduate students. Exercise care with this approach; the university may be motivated by gaining IP and publications, so ensure your agreement is appropriate. Students who have worked with you on a part-time basis in this manner are more likely to seek future opportunities with you. Similarly, there is a growing trend for Masters and PhD students to work part-time alongside their studies. If you can offer flexibility in this regard, you may attract exceptional candidates who are not seeking full-time work.
Many universities offer excellent AI programmes and candidates. Examine their research pages and identify labs working on problems similar to yours. A small sub-set of universities with high quality AI programs includes:
4. Investors: If you have secured investment, leverage your investors. Ask them to introduce you to other AI- led companies in their portfolio so you can share ideas and recruitment opportunities. There may be excellent candidates who are no longer a fit for other companies – for example, due to a relocation – who would be ideal for yours.
5. Job Boards: Job boards can be effective at attracting applications. However, with a public posting on a generic job board you are likely to receive numerous applications that are poorer in quality or fit. The time, and therefore cost, required for an individual in your company to review them can be considerable. Specialist AI job boards, including those on Kaggle (www.kaggle.com/jobs) and StackOverflow (https://stackoverflow.com/jobs?q=AI) are typically searched only by people already part of these communities and typically offer higher-quality candidates.
Many problematic and embarrassing AI systems have been developed because the teams that created them lacked diversity. Without different perspectives on data and results, you may create systems that offer narrow appeal and broad offence. Review the culture in your company, AI team and hiring practices to ensure diversity, representation and inclusion.
Further, in a competitive market for AI talent, leadership in diversity will enable you to attract exceptional candidates who may otherwise have overlooked your position – or been minded to accept an alternative.
When hiring, ensure you understand the role, seniority and the minimum requirements for which you are hiring. If your team uses Python exclusively, for example, do not hire someone who only works in R or Matlab. Missing skills will impact your costs directly, as individuals take time to address gaps. Describe the projects on which the successful candidate will work. Do they relate to computer vision, language, prediction, generation or other? Use the industry-standard terms of classification, regression, generative AI, sequencing and clustering for easier comprehension. Describe the expectation for the role as well as the difficulty of the problem.
You will also need to sell your company and the domain. Most data scientists seek work that will ‘make a difference’. To attract talent, frame your problem to demonstrate how the successful candidate’s work will do so.
Source: MMC Ventures
Hiring exceptional talent is challenging. While it can be tempting to prioritise mathematics candidates with first class degrees, people with the largest number of academic papers, or those with the most appointments, consider your company’s needs. Exclude individuals with poor communication or collaboration skills, and people who cannot adapt to the fast pace and fluid nature of industry. In addition to role-specific skills, a strong AI candidate will:
Seek adaptable, intelligent problem solvers – not individuals limited to following TensorFlow tutorials. Are you recruiting an individual to undertake research, or do you need someone to obtain insights from data? Depending upon the problems to be solved, limited academic experience may be unproblematic if the individual possesses required skills. Individuals may have gained experience through alternative initiatives, including hackathons and competitions.
While the interview process for AI roles is similar to the process for other technical roles, there are differences. A conventional developer, for example, would undertake a technical test at a face-to-face meeting. AI candidates cannot demonstrate their ability to build an AI model at an interview given the time constraints. Involve existing members of your AI team throughout the process. The best candidates will complement existing ideas while bringing something new to your team.
The candidate is given a set of 50 frames from a video. Some are identical; some close; some have the same composition but different subjects; and some are unique.
Source: MMC Ventures
AI talent is in short supply. When you have attracted high quality professionals to your team, it is important to retain them. While an attractive financial package and benefits are necessary, large companies can – and will – offer higher salaries. Retain AI talent by catering to team members’ other needs: