Post-Funding Hiring: Critical Startup Mistakes That Often Go Unnoticed

Congratulations, you’ve secured a round of funding. It was not easy, especially in today's economy. But the hardest part and the real test is still ahead.  

You convinced the investors; now, it's time to “convince the market”. This includes attracting customers, partners, as well as future team members. To deliver on your promises you need to grow your team. Let's examine 8 often overlooked recruitment mistakes of post-funding startups.

What qualifies us to provide advice in this area. What’s different about it?

This list is derived from many years of experience with recruitment projects undertaken by ChallengeRocket in the IT sector in Europe, the US, and Japan, as well as extensive conversations with founders and managers in these regions. 

In this article we seek to go beyond the obvious often-repeated advice and focus on actionable insights, supported by actual research and real-life examples whenever possible.

What if you don’t agree? (spoiler: you may be the next Sam Altman)

The list below is not entirely contrarian advice, but there are some points you may definitely not agree with - like why wouldn’t you like to hire the most talented candidate? Write them in a message, or who knows, maybe we'll get a chance to discuss it personally over coffee at some conference 🙂

When it comes to startups there are certain truths that are almost universally acceptable, like:

• Don't give away too many shares at the beginning (bootstrapping)
• Know your customers when building the product
• Test product with customers during development
• Release your product early and improve on the go (“better done than perfect”)

etc.

Nobody questions that advice. 

But then, someone comes along who breaks all these rules in an extreme way, resulting in the biggest startup success the world has seen in recent years—OpenAI (on its way to a nearly $30B valuation as I write this). It’s really mind-blowing when you but you think about it but, Sam Altman, in creating ChatGPT, defied all conventional startup wisdom:

• raised a lot of capital and gave up a lot of shares even before the first version of the product
• didn’t know who their customers were going to be 
• did not know what they would use it for
• did not know what the business model is going to be
• didn't consult with clients about the product
• and took 4.5y to launch the 1st version of the product!
• when finally released after >4 years it had no social features or built-in sharing!

The list goes on…

What's the takeaway? Everyone finds their own path in this journey.

Below I am merely showing you my path and my point of view based on my experience in this sector. Pick what works for you!

1. Rushed Hiring Decisions After Funding

Funding is closed. Let’s start the game! And the name of the game is speed.

In a newly funded startup it’s all about the speed. Often, it’s not the best decision-makers who win; rather, it’s the fastest ones who come out on top. Both founders and investors are eager to see progress, and there’s an urgency to capitalize on market opportunity before it vanishes. In this environment, hiring can become a rapid-fire process. 

It’s tempting to quickly fill positions to maintain momentum. The problem is that mistakes made in hiring key personnel (i.e. your CTO and first developers), can have long-lasting consequences, affecting the company bottomline for months or even years. The so-called technical debt incurred at the early stage is the hardest to repay. Just invest an extra week to thoroughly interview and evaluate more candidates. Or invest in professional support in the process, so you can move faster without sacrificing the quality of talent you recruit.

Rule: Don’t let the excitement or investors pressure to deliver on promises to push you to make hasty hiring decisions.

There is an old concept in business that you definitely should consider: hire slow and fire quickly.

2. Building too big IT teams.

Before being acquired by Facebook, WhatsApp had around 55 employees for its 900M users. 

Yes, they scaled to nearly 1 billion users with just over 50 people in the entire company! This demonstrates the power of lean teams. WhatsApp is an exceptional example but not an isolated one. Many founders don't fully realize how small the core technology teams of successful startups were when they already had millions of users. 

Of course if clients want to hire more engineers, it's beneficial for us at ChallengeRocket, but the reality is that it's often redundant. We've observed many times that startups tend to overestimate the size of their IT teams, resulting in unnecessarily expensive and inefficient groups. We’ve been putting together a lot of tech teams that despite their smaller size, have outperformed larger ones. 

Now, you might claim that WhatsApp is a fairly simple app and your project is something way more complex. So first of all it probably is not. It may appear like it because the simplest technology experiences involve well-engineered systems that hide complexity from the end user. But for the sake of discussion let’s say you’re building something bigger and more complex. In this case consider using multiple independent smaller teams rather than a single big team. I recommend reading more about Spotify or GitHub models and how they employ small, autonomous squads with a strong sense of ownership. 

Smaller teams often outpace larger, less agile ones. They usually also develop stronger bonds and a shared sense of purpose, which translates into lower turnover. WhatsApp is no longer a 50 person company. As of 2024, they have over 1,800 employees which is by the way still a relatively small team considering a user base of over 2.7 billion monthly active users. Companies, especially after IPOs, often grow their engineering teams significantly. Still excessive growth and low revenue-per-employee ratios can be warning signs.

A classic example is Blockbuster compared to Netflix: In 2004, Blockbuster had 84,300 employees for $1.2 billion in revenue, which, when adjusted for inflation, is about $1.56 billion in 2017 dollars. In 2017, Netflix had 5,400 employees for $11.6 billion in revenue (yeah, that was the golden year for Netflix). This means that Netflix had approximately 15.61 times fewer employees and 7.44 times more revenue, making each Netflix employee approximately ~116 times more productive.

Small teams tend to be more efficient. Efficiency is essential. Early-stage efficiency is critical.

Rule: Keep it lean. Consult optimal team composition if necessary.

3. Over-hiring in general. Hiring based on speculative growth rather than actual demand.

The allure of rapid scaling can be seductive. However the excitement and pressure to scale quickly can lead to bringing on more employees than necessary.

A textbook case of over-hiring is WeWork, one of Silicon Valley's biggest failure stories ever. Company emerged with a big idea: to make offices different and unite creative minds from around the globe. Investors were captivated by this mission, resulting in $13.8 billion in funding, including a $4.4 billion megaround. 

WeWork hired thousands of new employees post-funding without aligning workforce growth with revenue. Meteoric rise was followed by a spectacular fall. Over-hiring, an unsustainable business model, and financial mismanagement eventually led to massive layoffs (~2,400 employees in 2019 alone). The company, once valued at $47 billion, suffered a staggering 98% drop in its share price in just one year.

Ultimately, WeWork declared bankruptcy, marking a dramatic end for one of the once most promising startups. From their story, we can learn a crucial rule: “Hire for Need, Not Anticipation.” What to do instead of hiring for expected demand? Create and nurture a talent pipeline.

If you anticipate future growth you may organize tech meetups or use hackathons to build relationships with future hires. This way you can also connect with those who are not immediately available for hire. If you’d like to see what’s our approach to hackathons scroll down to FAQ here.

Rule: Actively engage with potential future candidates, even when there are no immediate job openings.

4. "I've spent enough time on recruitment tasks today - need to get back to work now" mentality

We’ve heard variations of this statement many times after delivering a pool of candidates to a company.

“Please schedule candidates for next week, we have a lot of work to do now”. 

Recruitment is your work. In fact, after early funding recruitment might be the most critical work you need to do now. You may say I take it too literally. At first glance, it seems like an innocent remark – someone is simply stating they need to return to their regular project tasks. 

So what’s wrong with wanting to catch up on their usual duties? Problem is that these words often reflect a certain mindset that translates to an approach where candidate interviews are squeezed into spare moments, trying to fit them around what is considered “main work.” 

As a result, when a good candidate appears, instead of scheduling an interview for tomorrow, it gets pushed to “next Wednesday”. Then the follow-up happens a week later. The main issue with this isn't that the specific candidate is waiting too long. 

To see the main problem consider the following scenario:

a) We (employer) start the process by speaking with a few candidates in the first batch. 

b) Candidate X seems to be a great match, but we don’t want to make a hiring decision until we interview some more. To be sure it’s the best choice. 

c) Then we have "other tasks," so the next CVs are analyzed a couple of days later because that’s when we have free slots.

d) The following interviews are also scheduled a few days apart, and so on...

Effect: Finally, it turns out that X was the best, but enough time has passed that this candidate is no longer available.

Rule: compress it as much as you can.

The approach we recommend is to dedicate a few days almost exclusively to recruitment, compressing everything as much as possible. Otherwise the whole process gets stretched out and candidates who were in the funnel at the beginning of the recruitment timeline may drop out because they have already found better opportunities.

Finally we understand that these principles are easier said than done.

To make it easier:

➡️ Find people you trust who will support you in this process, especially if you’ve got a lot on your plate.

➡️ Also in order to have many solid candidates simultaneously in the process (so that you can compress it all within 2-3 weeks rather than 2-3 months) you might want to rely on external support.

When I comes to source of reliable pre-screened candidates my recommendation as founder of ChallengeRocket will not be objective here but I’d point to what we do in 2 areas:

• Direct Hire
• Recruitment Challenges

In short:

1. Observe your thoughts

• are you subconsciously thinking that you wasted time talking to a candidate you won’t hire?
• do you see something else as critical work?

This is probably not the case.

2. Compress the process. Generate a big pool of candidates fast so you can avoid spreading interviews over many days.

5. Prioritizing the smartest candidates (top skilled, the fastest-learners etc.)

In tech recruitment, skills are usually the primary focus. That sounds correct, right?

We ourselves use specialized tests on the ChallengeRocket platform to evaluate these skills and candidate potential. Everyone aims to hire the best talent. 

However, bringing on board the top talent can paradoxically be a mistake. In reality, unless you’re building the next OpenAI, you probably don't necessarily need the best engineer in the world. You need a solid engineer. And you need the most responsible engineer and loyal person- someone you will be able to count on. Someone who will not abandon you in the middle of the project when things get tough. Not necessarily a top tech rockstsar. 

Startups are a rollercoaster ride with ups and downs. It's crucial to assess whether a candidate will stick with you through challenges or be the first to jump ship.

“Loyalty factor” is not easy to assess. It requires experience you get after many years. When we participate in Direct Hire projects for clients, we consider this factor carefully. Often, we recommend not the technically best candidate, but the one who is the most emotionally stable and reliable.

In short:

Highlighting top talent without factoring in loyalty can be a critical mistake.

It's not just about finding a top tech rockstar but someone technically solid but most of all loyal and dependable.

6. Poor pre-screening tests that in extreme cases cause good candidates to drop out of the process.

Automated tests are like a sharp knife. They can be a useful tool, but if used incorrectly, they can cause harm. 

A good automated test minimizes your time and recruitment costs by performing an initial screening and giving you a measured talent pool. 

A poor or improperly selected test can cause damage:

• It might pass candidates to the next round who are not a good technical fit.
• At the same time, it might overlook good talents.
• Poorly chosen tests (e.g., university-style exams) can deter good senior candidates.

A good test is hard to make because it needs to meet several criteria simultaneously:

• Predict future job performance.
• Provide automatic scoring.
• Not take excessive time from the candidate.
• Provide a good candidate experience.
• Be difficult to cheat (i.e., solutions via ChatGPT).

Usually the best indicators and predictors of job success are not abstract tests but those that closely simulate simplified job situations—like an engineering problem in a virtual coding environment. To simplify, if you need a "solid engineer" type of programmer whose task will be to implement specifications according to good design patterns, give them a test that simulates such a task. Do not give them a task that mainly focuses on creating efficient and optimal algorithms, and vice versa.

Allow me a moment for a brief advertisement here because I am proud to say that at ChallengeRocket, we were the first to develop technology for tests where candidates cannot cheat using ChatGPT.

If you want to see a sample of such a test, feel free to visit us here!

Beyond pre-hire tests:

Naturally, the entire assessment process should be disciplined and clearly communicated to candidates upfront, and as compressed in time as possible.

If it's not, and the process drags on for months, candidates who were initially interested will drop out, finding other opportunities in the meantime.

7. Not hiring truly original people (next is the test you can use) 

If you’re doing something original, hire original people.

This may be risky to have someone with contrarian points of view and that is why it’s NOT what big companies do. Big companies play a different game than you (unless it was a C-series that you raised). They have mature products with established processes around them and play it safe aiming to “reduce the variability of outcomes”. Their main priority is to prevent major failures, even if it means sacrificing potential high rewards.

They steer clear of rollercoaster rides; opting for a steady, flat path that may not provide dramatic highs but also protects against sudden drops.

If you're a startup company, consider adopting the opposite strategy because unlike large corporations, your success does not hinge on avoiding mistakes. So are you bringing on people who truly have unique and original perspectives? How to assess originality as a trait?

Let's assume you are developing a product in the EdTech sector. Ask your candidates to share a belief about this field that isn't a conventional truth. Answers like "learning should be personalized”, “interactive content improves engagement”, "gamification makes learning fun” etc. are cool, but also commonly known and widely accepted. 

The question isn't whether they can say something that most in the industry agree with, but whether they can present an interesting idea that many might disagree with.

Very few people will be able to do so, but they are the ones who can challenge the status quo and should be sought after. Read some works of Peter Thiel for more inspiration in this area! And naturally, also seek out genuinely passionate individuals - those who attend hackathons and spend their free time working on innovative and exciting projects might be the ones.

8. Using recruitment agencies that do not provide any added value beyond basic LinkedIn search

What I’m about to say might not sit well with many people, but often, recruiters don’t have their own network of developers and do nothing beyond mass messaging on LinkedIn.

They then pass on “recommended CVs” without understanding the candidates or their fit for the role. This is simply because they don't grasp the nuances of specific job roles, the underlying technologies, or the potential for transferable skills. This approach has very limited value. If you want a basic LinkedIn search, you can do it yourself or use tools for automated messaging.

If you engage external partner look for added value beyond that, such as:

1. Access to passive candidates that aren’t easily reached through LinkedIn.
2. At ChallengeRocket, for example, we spend a lot of time organizing meetups & hackathons to build a unique talent pool. That pays off later as we can connect employers with talent that is very hard-to-reach otherwise.
3. Pre-Assessment preliminary selection - so that you get narrowed-down pools of qualified candidates. This process however shouldn't just be about verifying if someone has X years of experience in a required technology. That's not enough.
4. Market insight and advice - ideally a partner should give you insights and knowledge of the local employment market / standards / align your offer if necessary etc. 

Aim for no less than that!

An additional related mistake is the lack of awareness of bad practices that may be used by some recruitment agencies. Agencies often get paid for placements and may engage in unethical practices to push their candidates. Some of them are quite obvious, such as altering or exaggerating the qualifications on candidates' CVs or withholding certain information.

Others are more subtle. For example, an agency might send an initial candidate who is not the best fit to gather information about the interview questions and tasks. They then use this information to prepare other candidates, giving them an unfair advantage. Therefore, it's crucial to audit, look carefully, and choose your recruitment agency with diligence to ensure you partner with an ethical and reliable firm.

Disclaimer:

What we describe here is not representative of the entire industry. There are many agencies that operate ethically, maintain high standards and perform their roles exceptionally well. These agencies genuinely work to match candidates with suitable job opportunities. However, it's important for employers to remain vigilant and aware of instances of unethical behavior in the industry. 

Bringing these practices to light and speaking openly about them can raise standards across the entire industry.

That’s all for now! Let me know in the comments if you’d like to read more points.

If you’d like to know more about our approach you can meet us at webinars or write to us directly using the contact form below!