Recently I was having dinner with a fellow startup founder who has been having trouble recruiting and inspiring engineers here in the Valley.
Then he asked me point blank:
You’ve had so much success building a team of awesome engineers, much of which are masters and PhD level…What’s your secret?
I don’t think there is any one secret that makes building technical teams instant successes. I told him that I would think about what has and hasn’t worked over the past couple of years for me and share it with him.
Here’s what I’ve found.
First — It all starts with leadership. You can have the best team, the best technology, but if you can’t inspire people to work on your vision, you’re not going anywhere.
For example, I’m passionate about using technology to improve peoples’ health and well-being. By sharing this excitement during the hiring process, you can tell if the potential engineer has that same fire inside. If they are in it for the paycheck…Don’t waste your time.
Leaders should also be transparent and overly communicate the priorities that will make the company and the team’s lives better. This means sharing the good and the bad so people have the proper perspective to feel like they belong and can help where necessary.
Second — Get your incentives right. The power of incentives are one of the strongest human drivers and need to be tailored for each person. Incentives don’t have to be just money, they could be flexibility in working hours, projects, and other job responsibilities.
During the interview process, make sure you spend enough time upfront to understand the candidate and really dig deep to find the why for which they want to work for your company. Motivation becomes easy once you know the incentive that keeps them excited to come into work each day.
Third — Outstanding teams build outstanding technology. Think of yourself as a coach. Your goal is to build a company with an impressive team to build game changing technology for the sole purpose of improving the lives of your clients.
View the interview process like tryouts. Before hiring, make sure you seek the candidates opinion and really pick their brain for how they would approach the problem. Even better, give them a small portion of the problem and see what they come back with.
From there you can start determining what position they can play to strengthen the existing team. Don’t put someone in a position they are not passionate about. They will only waste time, money and bring down the team.
Fourth — Instill a culture of consistent improvement and knowledge sharing. Especially when you hire PhDs, they tend to be very specialized in a singular domain. Make sure you have your team constantly share their problem solving approaches to broaden the group’s perspectives for future problems.
This also has a side-effect of taking the pressure off of one person to not be afraid of asking the group for help. Strive to not waste time and resources and use multiple brains when necessary. Getting multiple brains on a problem yields great ideas, products and a stronger team.
Fifth — Engineers like to feel like superstars. This goes back to incentives. Give them a platform to own their stage within the group and reward them promptly for best results. Delayed rewards or punishments aren’t as effective.
When you give them the responsibility to own their work, they tend to end up coordinating their own timeline without much hassle. This helps the leaders reduce their stress and focus more on client management and bringing in more projects.
Lastly — Talent finds teams. Build and foster a great engineering culture and you’ll see that even highly paid engineers at top tech companies will want to work for you. At some point engineers find that it’s not about the money, but the internal drivers that get them excited to wake up in the morning and tackle interesting problems that build great engineering companies.
About the author: Shalini Ananda, PhD. is a data scientist and advanced mathematician with a background in image recognition and computational chemistry.
She is also a deep learning engineer and inventor of a number of commercialized technologies.