To hire best-in-class IT talent, your company must have interesting technical problems to solve.

Adam Sypniewski, CTO, Deepgram

April 25, 2022

5 Min Read
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In a job seeker’s market, we are seeing more and more IT candidates looking for jobs that excite them. To remain competitive when looking for top talent, companies must constantly evaluate the best ways to recruit and retain vital IT candidates. One of the best ways you can achieve this is to ensure that your company has exciting problems people will be keen to solve. As a chief technology officer, I’m not just thinking about the technical problems we want to solve, but how we can attract the best-fit talent to help us solve them. Below are three crucial elements to consider when recruiting technical employees in a hyper-competitive economy.

1. Define the problems you want to solve

Hiring managers sometimes forget that people want to work on interesting problems. Suppose your company is not challenging the status quo in some way technically or performing research or running experiments. In that case, it will be difficult to attract highly ambitious and motivated candidates interested in solving bigger, more complex issues.

Here are some types of valuable problems that you can solve at any company:

  • Solved problems: Solved problems have an existing model you can easily repurpose to develop solutions. For example, if you want to design a fast-food chain, you have readily available resources to learn from and model your business after.

  • Solvable problems: Solvable problems are typically hard to solve, but it’s not impossible to arrive at a solution. There are usually frameworks that exist, but you need an engineer to tweak them for the specific problem you’re addressing. An example of a solvable problem would be when auto companies were building the first electric car -- all the pieces of information to do so were there. It was just a matter of piecing together and ironing out kinks.

  • Unsolved problems: Unsolved problems don’t have an existing model or framework to solve them. We can take a stab at building a model for these kinds of problems, but it’s going to require plenty of learning and mistakes before we get there.

While you’re identifying the problems you face and deciding which subset you want to work on, it’s equally important to pick the right tools for the job. You want employees to feel empowered on what they’re working on. Giving them tools they love to use, like one of the newer, more popular programming languages Rust, makes them even more excited to dive into the work they're assigned. From there, you’ll have a better chance of building a successful team.

2. Be realistic about the team you need to get the job done

Once you have categorized the problems you want to solve, you need to think about the team to get the job done. You want to ensure your budget goes as far as possible while also acknowledging that prospects are looking for competitive compensation. Do you need data scientists, engineers, or both? It entirely depends on what your company is trying to accomplish. Researchers wouldn’t help with already solved problems, but they would be critical in addressing unsolved problems: Why do you need the best researchers if you’re hoping to be the next hot software as a service (SaaS) platform? If you have solvable problems, you’d ideally avoid building a robust engineering and research team since their talents would be unchallenged.

It is essential to have an incredibly high bar for hiring: the initial team will need to operate semi-autonomously and be trusted to make excellent short- and long-term decisions. That is why you should never stack your team with managers or directors first -- instead, hyper focus on hiring from the bottom up. You need people to solve the deep, hard problems every day, not project managers or team coordinators. As the demand from customers and prospects heat up, you’ll need more engineering capacity. You’ll eventually see the need for processes to be put in place to maintain productivity: multiple teams working on more specialized problems to keep every engineer engaged and productive. Remember, when building out a strong team, you need to ensure you have people you trust and are excited to have on board.

3. If it’s not a ‘Hell Yes,’ it’s a ‘Hell No’

Your top priority should be hiring the very best people you can without being afraid to be selective. I like to say, “if it’s not a hell yes, it’s a hell no.” It might not be blatantly obvious to some, but a fantastic company requires terrific people -- that’s not to criticize a lot of candidates, but there are very few excellent ones. And when you can only hire a certain number of people, it’s essential to be even more cautious. It can be tempting to quickly hire a pretty good candidate in those moments when you’re feeling the strain of being understaffed, but you’ll likely regret it in both the short term and long term. Let everything else burn, and don’t try to extinguish the pain of being short-handed by hiring reactively.

In-demand candidates typically have high expectations of how they’d like to spend their time and what resources will be available to them, which may not align with your company’s goals. For example, if you hire a candidate who loves to present at conferences, they might not be the best person to help with entirely internal projects, even if their experience and credentials are impressive. Put another way, it’s crucial to hire people whose expectations match the resources and problems you have and who are excited and interested to tackle them.

In today’s world, hiring top-tier talent for technical roles can be incredibly challenging -- but identifying the problems your company wants to solve is the first step. From there, it’s critical to select the right tools for the job and assemble a team that will be excited to work on the types of problems you are solving. Regardless of budget, do not hire from desperation -- and keep the bar for talent high.

About the Author(s)

Adam Sypniewski

CTO, Deepgram

Adam Sypniewski is currently the CTO at Deepgram, an end-to-end deep learning ASR company. Adam has designed artificial intelligence systems for autonomous vehicles and built next-gen technology for The Defense Advanced Research Projects Agency (DARPA). He earned his Ph.D. in experimental astrophysics from the University of Michigan, where he used machine learning to understand dark energy.

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