Robin.ly and TalentSeer and held its 2019 AI Talent Summit: Building Teams for The Future of AI on September 19, at the Computer History Museum in Mountain View, California. In face of the rapid advancement of AI technology and commercial breakthroughs, this summit was convened to discuss AI talent insights with startups, VCs and corporates; allowing them to “have a better understanding, not only of the current talent market but also the various tactics in building and scaling a unique, resilient, and diverse team”. A prominent lineup of speakers from top startups and VCs shared inspiring insights and experience with 300 tech and talent leaders from a variety of backgrounds at the event.
Technology transformation yields surging demand for AI expertise
Gil Arditi, Head of machine learning product at Lyft, spoke extensively on the progression of AI/ML technologies, with examples from speech to speech technology to perception applications in the medical industry. We are currently experiencing a shift from merely startups using AI to it becoming widely available on an industry-scale. AI is entering its commercial phase, which means that demand is higher than ever, and expected only to keep growing. AI is critical for solving a wide range of problems that businesses can no longer ignore; even companies that aren’t AI-based are beginning to look towards machine learning to gain an advantage. Using an analogy of human evolution, Gil stated, the development of AI technologies is still in the “primordial soup phase”, but “it shows the potential of what could come up ahead and how strong and compelling it can be across many domains.”
AI talent market is getting more competitive
Georgina Salamy, director of talent at Zoox, discussed the competitive nature of the AI market, comparing her experience in the auto industry between Tesla and Zoox. 10 years ago, Tesla’s unique position amid the crowd of software and internet companies was an advantage when pitching to talent. “This is a different generation of talent in today's AI market. They have much more choices. They care more about perks and the challenge. What's the career progression? How does my role influence decision making?” The shift in the industry landscape makes it much more challenging to acquire talent.
Margaret Laffan, VP of Business Development at TalentSeer provided insights on the competitive market based on TalentSeer’s experience working with 100+ AI companies and thousands of candidates. “Candidates actively looking typically have 3-5 offers, and even passive candidates would have 1-2 offers. It typically takes 1-3 months to hire, but some candidates can be on-boarded within 2 weeks and the turnaround time on some offers can be a matter of hours.”
Acquiring talent in the AI era requires an innovative mindset
The most common way for recruiters to attract new talent is to fill their inbox with information about the employer and position. However, the conversion rate has been consistently low as almost every AI engineer has an overloaded inbox with repetitive pitch messages. Alex Ren, founder of TalentSeer & Robin.ly and managing partner of BoomingStart Venture, shared an innovative “3i Recruiting Mindset” that has been proven effective in today’s AI talent market. Inspired by Simon Sinek’s “Start With Why” framework, insight-based and influence-based recruiting are elevated from information-based recruiting. Sharing in-depth insights on industry, business, and career development through various platforms will help amplify the impact at scale and build your own thought leadership.
Building & scaling a robust & resilient AI team
Whilst there is no one size fits all model, Margaret illustrates a generic team structure based on TalentSeer’s experience helping over 100 companies building out their engineering teams. Different types of AI companies need to recognize their own strengths and weaknesses to develop a resilient team for business success. “For example, industry AI companies with strong expertise in commercialization and application need to work on building a solid data and core algorithm team to make the business model work.”
Luan Lam, Global Vice President of Talent at Harness and the Talent Advisor for Unusual Ventures, introduced a unique “recruiting as a sales process” approach that he has been utilized to successfully scale several technology companies, from a hundred to thousands of members.
Jennifer Holmstrom, talent partner at GGV Capital, showcased the outcome-driven “scorecard” method found useful for generating cohesive hiring results across founders, hiring managers, investors, and board members, especially for executive hire.
Georgina Salamy also explained the importance of finding talent who can “stick it out during some tough times”. While technical ability is obviously sought after, in many cases it is something that can be picked up at the company level. What cannot be taught, however, is to have a passion for solving a challenging problem.
Nurturing work experience is key to retaining your top talent
Retaining talent is equally important for AI companies and teams given the competitive market and limited talent pool. Jennifer Holmstrom shared valuable insights on retaining top talent based on her experience at VCs and Facebook. “The number one reason people leave companies: people leave people. People leave their manager way more often than they actually leave the company.” Managing decision context such as “Why decisions are made, why certain customers are performing for the other?” is enormously helpful for unifying the team.
Luan Lam also emphasized the importance of “bridging the gap between the candidate journey into the employee journey” for retention. “Studies have shown that having successfully onboard someone on their first day actually determines longevity of their career within the company.”
To add, Margaret Laffan outlined the top 5 reasons talent leave an AI team from TalentSeer’s interactions with thousands of candidates:
1) The engineering team is not strong enough to build infrastructure;
2) Not enough data to train machine learning/deep learning models;
3) The problem is too challenging and cannot be solved in a foreseeable timeframe;
4) Unrealistic expectations about what can be achieved and
5) Not having an impact.
Diversity is the catalyst for a successful team
Diversity and inclusion in the workplace is a significant topic, which covers gender, background, education, ethnicity, experience, etc. Georgina Salamy noted that AI companies are developing tools that will affect all types of people, and to successfully do this, that means a wide array of different people need to be involved in the development process.
Ted Maguire, Director of Executive Talent at Khosla Ventures, also observed that many successful companies are built by getting senior people to join very early as they help guide others around pitfalls. Understanding the talent strengths in different countries also helps with establishing a remote team for outsourcing.
To build the optimal team under budget, Margaret suggested that companies plan carefully about perspective education and experience requirements. For example, you may need a Ph.D. to lead an algorithm team, but hiring a master in a supporting role may also produce great work and save 100k/year on an annual salary. Additionally, consider where you can hire talent beyond top universities like Stanford, MIT, CMU, etc. Look for AI talent in non-traditional places such as community colleges.
This summit is just the start of the conversation. There are still many topics within AI talent that need to be expanded on. We hope it will help evolve discussions and collaborations towards maintaining a flourishing AI ecosystem. One of the aims of the summit was to provide insights and expert opinions so that all members of the industry can stay informed and catch up with the latest news. This is ideal as it allows the industry to flow and communicate better, making it the perfect grounds to foster new ideas. The AI Summit is dedicated to supporting everyone in better understanding both the current market landscape and developments that it will experience in the future.