What happens when you have been consistently ordering a Margherita pizza and a Choco lava cake every time you visit Domino’s Pizza? Well, you know the exact order you want to place. You know the crust and toppings you want over your pizza. You don’t waste much time thinking about what to order. Isn’t it? That’s what happens with lateral hiring too. Lateral hiring is nothing but hiring specialists in your organization for a particular job role instead of investing time in training entry-level employees—you know the skills and experience level you need in a candidate to fill this job role. In other words, you know your order! Many organizations believe it’s good to have a few specialist employees who know every next step they take at their job. And that’s why the majority of recruiters are switching to lateral hiring. In this article, we share everything about what lateral hiring is and the 8-step process we follow at HackerEarth to conduct lateral hiring.

What is lateral hiring?

Lateral hiring is a method of finding an employee who can do a similar job to the vacant one—with a comparable experience and background. Simply put, lateral hiring is the process of sourcing passive candidates to fill niche, specialized or executive positions. However, you may not find these people in your talent pool or on job boards. They are neither active in the job market nor actively seeking opportunities. Earlier, such hiring used to happen in specific industries like law, medicine, big businesses, and the government sector. But, with the pandemic, many tech companies have adopted the unconventional method of lateral hiring to fill vacant positions. Adrienne Couch, Human Resources Analyst, LLC Services emphasizes the same.

“Lateral hiring is becoming more and more popular. In fact, I’ve seen studies that say it’s set to overgrow by over 20% in the next five years. Lateral hiring is also a great way for companies to tap into the passive candidate market. These are people who may not be looking for a new job but could be open to new opportunities. By reaching out to them, companies can snag top talent they may have missed out on otherwise.”

How does lateral hiring work?

Assess your workforce and conduct a skills gap analysis:

what skills do they currently have?
what skills are they currently missing (but crucial for the company’s growth)?

Once you have studied the skills, define the skills you need from your lateral hire. For example, after studying the skills of web developers in your company, you realize they are well-versed in four languages: JavaScript, C, C++, and SQL. But, you need to upgrade the product, which requires a PHP developer. Now you have two options to do this: train your existing employees, invest financial resources, time, and effort in them to learn it, or onboard a new hire with these skills. Next, define what the role of this lateral hire would look like:

what are the tasks and responsibilities they carry out?
what would their OKRs look like?
how would this role benefit the organization?

Why use lateral hiring to hire top tech talent?

In a study by University of Bristol – School of Economics, Finance and Management, it was found that lateral hiring helps employers acquire, develop and retain human capital—to help improve the competitiveness and reduce the effects of outward staff mobility. Therefore it should be included as a part of recruitment marketing strategy. Let’s understand in detail how lateral hiring can elevate the growth of employers and organizations:

Reduces training costs

There are two ways you can fill in the requirement for new skills in your organization:

train your current employees
recruit new employees

Training your employees requires a lot of heavy lifting. You need funds, time, and effort to prepare them and develop these skills. Still, there is no guarantee that your employees will master them correctly. They’ll take time to learn and implement the skills. This is exactly what Cynthia Davies, CEO and Founder of Cindy’s New Mexico LLC points out.

“Lateral hiring can be beneficial for a company as it reduces training costs. Employees who are already familiar with the company and its culture, as well as the industry in general, require less training. They can hit the ground running in their new roles. Lateral hires can be more efficient and productive in their new roles quicker than external hires because they already know the company’s processes and systems.”

Reduces the risk of hiring the wrong person

Picture this: you want to hire a web developer with PHP expertise the traditional way. You post on job boards, publish the job ad and receive several applications. You scan the applications and shortlist a few developers. They have *only* basic knowledge of the subject and aren’t up-to-date with industry trends. Upon onboarding them, you realize that you still need to train them. Why? Because they have a strong theoretical understanding of PHP but need training on a few practical aspects of the job. That’s when you realize you have hired the wrong person. You needed an experienced employee who knows the ins and outs of what works and what does not and is not restricted to entry-level PHP expertise. With lateral hiring, you can scan the candidate’s profile, and check if their experience fits your requirements. Only then proceed with the next steps in your hiring process.

Also, read: 10 Tech Recruiting Strategies to Find the Best Tech Talent

The 8-step process for lateral hiring at HackerEarth looks like

Preethi Saakre, the Talent Acquisition Manager at HackerEarth shares an extensive 8-step approach for lateral hiring that we use in our organization.

Step 1: Get approval for the role

As a first step, the hiring manager identifies and approves the job role and the skills needed for a lateral hire—which is shared with the talent acquisition team. The talent acquisition (TA) team then works with the hiring manager to confirm the job description (JD). If it’s an existing role, they check with the hiring manager if any changes need to be made to the existing JD. If it’s a brand-new role, the hiring manager will share the roles and responsibilities, and the TA will add these roles and responsibilities to the set template. Next, the talent acquisition team creates the position on Trakstar and sends it for the CEO’s approval.

Step 2: Collect important details

Once the lateral hire’s position is approved, the talent acquisition team conducts an intake call with the hiring manager to understand the need for the role. To collect details about the job role, the TA team fills out a Requirement Gathering Form. They enter all the necessary details and share them with the hiring manager. Next, the hiring manager identifies panel members to be involved in the hiring process. They make sure that each member is aware of the expectations of the candidates and will be interviewing the candidates based on these expectations. They also identify who will handle the competency evaluation process at different levels of interviews.

Step 3: Share data about the talent pool

The talent acquisition team shares data on the overall talent pool available for the role with hiring managers. The TA team then creates a sample screened profile for the lateral candidate along with the hiring manager. This ensures that the sourced and inbound applications they share with the hiring manager are in line with their expectations.

Also, read: Optimize your Hiring Process with Recruitment Analytics

Step 4: Publish the approved position

The talent acquisition team publishes the approved position on job boards—LinkedIn, Glassdoor, IIM Jobs, and Instahyre. On the same day, the talent acquisition team shares the lateral hiring strategy and plan with timelines. They send an email to the hiring manager with the finalized timelines and the other details discussed over the call.

Step 5: Screen the candidates

The talent acquisition team conducts a candidate screening check. During the process, they make sure to follow all the set guidelines:

checking the communication skills
asking the role screening questions
communicating the CTC and notice period expectations
evaluating for culture fit
understanding the reason for leaving

Once they have screened the candidates, they’ll forward the candidate’s application to the hiring manager for review. Sidenote: If the candidate’s salary expectation is more than the budget for the specialized role, TA needs to communicate it with the hiring manager before proceeding with the conversation.

Also, read: 4 Different Ways to Create Coding Tests on HackerEarth (+Free Template)

Step 6: Use the STAR technique for interviews

Our recruiters and hiring managers at HackerEarth use the STAR technique to answer behavioral questions. When hiring managers ask behavioral questions to candidates, candidates have to give them examples of how they handled past situations or challenges. Put simply, behavioral questions help candidates share stories. Interviewers use these stories to identify the evaluate the candidate beyond their skills.

For example, the HackerEarth interviewer asks the candidate: “Can you tell me about a time when you went above and beyond to deliver an excellent customer experience?” Situation: “When I was working at company X, we were preparing for a video interview for a client when I learned that someone on their team was deaf. The presentation was due the next day in the morning and I was the only one left in the office after 5 PM.” Task: “I realized there was only one solution and that was for me to stay behind in the evening and add captions myself.” Action: “It took me a few hours, and around 8 PM, I was done. Then, I let our team know about the update.” Results: “In the end, the client enjoyed the presentation. They were very impressed (and surprised) by our attention to detail, and we ended up closing them soon after.” With this story the candidate shared with the interviewer, the interviewer learns about the candidate’s willingness to deal with challenging situations and their passion for work. Once the interviewers complete the interviews, they share detailed feedback and ratings on the same day. Sidenote: The interviewers record feedback on Trackstar.

Also, read: Essential Questions To Ask When Recruiting Developers Part 1 and Part 2

Step 7: Conduct reference checks

Reference checks ensure that the lateral hire the company has recruited is the right decision. For this, recruiters reach out to the hire’s colleagues and ask them about the following:

their experience working with the person
the candidate’s performance while working with the organization

Answering these two questions give ample information about whether or not the right decision has been made.

Step 8: Send the offer letter

First, we make a verbal offer to the candidate, followed by an offer letter via Adobe. The candidate reviews the offer letter, signs it and it automatically gets recorded in the company’s records and the candidate receives a copy of the signed offer letter. Here’s how our talent acquisition team sent the offer letter to the new hire via Adobe.

It’s not discreet. It’s not different

If you have ever been told to carry out the lateral hiring process discreetly, it’s time to change that. Lateral hiring is just like the traditional hiring process you carry out in your company. The only difference: you hire specialists. So, whether you’re hiring internal or external candidates as lateral hires, the process remains the same except for a few tweaks in some hiring phases and policies.

I Used AI to Build a "Simple Image Carousel" at VibeCodeArena. It Found 15+ Issues and Taught Me How to Fix Them.

‍

My Learning Journey

I wanted to understand what separates working code from good code. So I used VibeCodeArena.ai to pick a problem statement where different LLMs produce code for the same prompt. Upon landing on the main page of VibeCodeArena, I could see different challenges. Since I was interested in an Image carousal application, I picked the challenge with the prompt "Make a simple image carousel that lets users click 'next' and 'previous' buttons to cycle through images."

‍

Within seconds, I had code from multiple LLMs, including DeepSeek, Mistral, GPT, and Llama. Each code sample also had an objective evaluation score. I was pleasantly surprised to see so many solutions for the same problem. I picked gpt-oss-20b model from OpenAI. For this experiment, I wanted to focus on learning how to code better so either one of the LLMs could have worked. But VibeCodeArena can also be used to evaluate different LLMs to help make a decision about which model to use for what problem statement.

The model had produced a clean HTML, CSS, and JavaScript. The code looked professional. I could see the preview of the code by clicking on the render icon. It worked perfectly in my browser. The carousel was smooth, and the images loaded beautifully.

But was it actually good code?

I had no idea. That's when I decided to look at the evaluation metrics

What I Thought Was "Good Code"

A working image carousel with:

Clean, semantic HTML
Smooth CSS transitions
Keyboard navigation support
ARIA labels for accessibility
Error handling for failed images

It looked like something a senior developer would write. But I had questions:

Was it secure? Was it optimized? Would it scale? Were there better ways to structure it?

Without objective evaluation, I had no answers. So, I proceeded to look at the detailed evaluation metrics for this code

What VibeCodeArena's Evaluation Showed

The platform's objective evaluation revealed issues I never would have spotted:

Security Vulnerabilities (The Scary Ones)

No Content Security Policy (CSP): My carousel was wide open to XSS attacks. Anyone could inject malicious scripts through the image URLs or manipulate the DOM. VibeCodeArena flagged this immediately and recommended implementing CSP headers.

Missing Input Validation: The platform pointed out that while the code handles image errors, it doesn't validate or sanitize the image sources. A malicious actor could potentially exploit this.

Hardcoded Configuration: Image URLs and settings were hardcoded directly in the code. The platform recommended using environment variables instead - a best practice I completely overlooked.

SQL Injection Vulnerability Patterns: Even though this carousel doesn't use a database, the platform flagged coding patterns that could lead to SQL injection in similar contexts. This kind of forward-thinking analysis helps prevent copy-paste security disasters.

Performance Problems (The Silent Killers)

DOM Structure Depth (15 levels): VibeCodeArena measured my DOM at 15 levels deep. I had no idea. This creates unnecessary rendering overhead that would get worse as the carousel scales.

Expensive DOM Queries: The JavaScript was repeatedly querying the DOM without caching results. Under load, this would create performance bottlenecks I'd never notice in local testing.

Missing Performance Optimizations: The platform provided a checklist of optimizations I didn't even know existed:

No DNS-prefetch hints for external image domains
Missing width/height attributes causing layout shift
No preload directives for critical resources
Missing CSS containment properties
No will-change property for animated elements

Each of these seems minor, but together they compound into a poor user experience.

Code Quality Issues (The Technical Debt)

High Nesting Depth (4 levels): My JavaScript had logic nested 4 levels deep. VibeCodeArena flagged this as a maintainability concern and suggested flattening the logic.

Overly Specific CSS Selectors (depth: 9): My CSS had selectors 9 levels deep, making it brittle and hard to refactor. I thought I was being thorough; I was actually creating maintenance nightmares.

Code Duplication (7.9%): The platform detected nearly 8% code duplication across files. That's technical debt accumulating from day one.

Moderate Maintainability Index (67.5): While not terrible, the platform showed there's significant room for improvement in code maintainability.

Missing Best Practices (The Professional Touches)

The platform also flagged missing elements that separate hobby projects from professional code:

No 'use strict' directive in JavaScript
Missing package.json for dependency management
No test files
Missing README documentation
No .gitignore or version control setup
Could use functional array methods for cleaner code
Missing CSS animations for enhanced UX

The "Aha" Moment

Here's what hit me: I had no framework for evaluating code quality beyond "does it work?"

The carousel functioned. It was accessible. It had error handling. But I couldn't tell you if it was secure, optimized, or maintainable.

VibeCodeArena gave me that framework. It didn't just point out problems, it taught me what production-ready code looks like.

My New Workflow: The Learning Loop

This is when I discovered the real power of the platform. Here's my process now:

Step 1: Generate Code Using VibeCodeArena

I start with a prompt and let the AI generate the initial solution. This gives me a working baseline.

Step 2: Analyze Across Several Metrics

I can get comprehensive analysis across:

Security vulnerabilities
Performance/Efficiency issues
Performance optimization opportunities
Code Quality improvements

This is where I learn. Each issue includes explanation of why it matters and how to fix it.

Step 3: Click "Challenge" and Improve

Here's the game-changer: I click the "Challenge" button and start fixing the issues based on the suggestions. This turns passive reading into active learning.

Do I implement CSP headers correctly? Does flattening the nested logic actually improve readability? What happens when I add dns-prefetch hints?

I can even use AI to help improve my code. For this action, I can use from a list of several available models that don't need to be the same one that generated the code. This helps me to explore which models are good at what kind of tasks.

For my experiment, I decided to work on two suggestions provided by VibeCodeArena by preloading critical CSS/JS resources with <link rel="preload"> for faster rendering in index.html and by adding explicit width and height attributes to images to prevent layout shift in index.html. The code editor gave me change summary before I submitted by code for evaluation.

Step 4: Submit for Evaluation

After making improvements, I submit my code for evaluation. Now I see:

What actually improved (and by how much)
What new issues I might have introduced
Where I still have room to grow

Step 5: Hey, I Can Beat AI

My changes helped improve the performance metric of this simple code from 82% to 83% - Yay! But this was just one small change. I now believe that by acting upon multiple suggestions, I can easily improve the quality of the code that I write versus just relying on prompts.

Each improvement can move me up the leaderboard. I'm not just learning in isolation—I'm seeing how my solutions compare to other developers and AI models.

So, this is the loop: Generate → Analyze → Challenge → Improve → Measure → Repeat.

Every iteration makes me better at both evaluating AI code and writing better prompts.

‍

‍

What This Means for Learning to Code with AI

This experience taught me three critical lessons:

1. Working ≠ Good Code

AI models are incredible at generating code that functions. But "it works" tells you nothing about security, performance, or maintainability.

The gap between "functional" and "production-ready" is where real learning happens. VibeCodeArena makes that gap visible and teachable.

2. Improvement Requires Measurement

I used to iterate on code blindly: "This seems better... I think?"

Now I know exactly what improved. When I flatten nested logic, I see the maintainability index go up. When I add CSP headers, I see security scores improve. When I optimize selectors, I see performance gains.

Measurement transforms vague improvement into concrete progress.

3. Competition Accelerates Learning

The leaderboard changed everything for me. I'm not just trying to write "good enough" code—I'm trying to climb past other developers and even beat the AI models.

This competitive element keeps me pushing to learn one more optimization, fix one more issue, implement one more best practice.

How the Platform Helps Me Become A Better Programmer

VibeCodeArena isn't just an evaluation tool—it's a structured learning environment. Here's what makes it effective:

Immediate Feedback: I see issues the moment I submit code, not weeks later in code review.

Contextual Education: Each issue comes with explanation and guidance. I learn why something matters, not just that it's wrong.

Iterative Improvement: The "Challenge" button transforms evaluation into action. I learn by doing, not just reading.

Measurable Progress: I can track my improvement over time—both in code quality scores and leaderboard position.

Comparative Learning: Seeing how my solutions stack up against others shows me what's possible and motivates me to reach higher.

What I've Learned So Far

Through this iterative process, I've gained practical knowledge I never would have developed just reading documentation:

How to implement Content Security Policy correctly
Why DOM depth matters for rendering performance
What CSS containment does and when to use it
How to structure code for better maintainability
Which performance optimizations actually make a difference

Each "Challenge" cycle teaches me something new. And because I'm measuring the impact, I know what actually works.

The Bottom Line

AI coding tools are incredible for generating starting points. But they don't produce high quality code and can't teach you what good code looks like or how to improve it.

VibeCodeArena bridges that gap by providing:

✓ Objective analysis that shows you what's actually wrong
✓ Educational feedback that explains why it matters
✓ A "Challenge" system that turns learning into action
✓ Measurable improvement tracking so you know what works
✓ Competitive motivation through leaderboards

My "simple image carousel" taught me an important lesson: The real skill isn't generating code with AI. It's knowing how to evaluate it, improve it, and learn from the process.

The future of AI-assisted development isn't just about prompting better. It's about developing the judgment to make AI-generated code production-ready. That requires structured learning, objective feedback, and iterative improvement. And that's exactly what VibeCodeArena delivers.

Here is a link to the code for the image carousal I used for my learning journey

#AIcoding #WebDevelopment #CodeQuality #VibeCoding #SoftwareEngineering #LearningToCode

‍

Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

The demand for mobile applications is exploding, but finding and verifying developers with proven, real-world skills is more difficult than ever. Traditional assessment methods often fall short, failing to replicate the complexities of modern mobile development.

Introducing a New Era in Mobile Assessment

At HackerEarth, we're closing this critical gap with two groundbreaking features, seamlessly integrated into our Full Stack IDE:

Now, assess mobile developers in their true native environment. Our enhanced Full Stack questions now offer full support for both Java and Kotlin, the core languages powering the Android ecosystem. This allows you to evaluate candidates on authentic, real-world app development skills, moving beyond theoretical knowledge to practical application.

Say goodbye to setup drama and tool-switching. Candidates can now build, test, and debug Android and React Native applications directly within the browser-based IDE. This seamless, in-browser experience provides a true-to-life evaluation, saving valuable time for both candidates and your hiring team.

Assess the Skills That Truly Matter

With native Android support, your assessments can now delve into a candidate's ability to write clean, efficient, and functional code in the languages professional developers use daily. Kotlin's rapid adoption makes proficiency in it a key indicator of a forward-thinking candidate ready for modern mobile development.

Breakup of Mobile development skills ~95% of mobile app dev happens through Java and Kotlin — This chart illustrates the importance of assessing proficiency in both modern (Kotlin) and established (Java) codebases.

Streamlining Your Assessment Workflow

The integrated mobile emulator fundamentally transforms the assessment process. By eliminating the friction of fragmented toolchains and complex local setups, we enable a faster, more effective evaluation and a superior candidate experience.

Old Fragmented Way vs. The New, Integrated Way — Visualize the stark difference: Our streamlined workflow removes technical hurdles, allowing candidates to focus purely on demonstrating their coding and problem-solving abilities.

Quantifiable Impact on Hiring Success

A seamless and authentic assessment environment isn't just a convenience, it's a powerful catalyst for efficiency and better hiring outcomes. By removing technical barriers, candidates can focus entirely on demonstrating their skills, leading to faster submissions and higher-quality signals for your recruiters and hiring managers.

A Better Experience for Everyone

Our new features are meticulously designed to benefit the entire hiring ecosystem:

For Recruiters & Hiring Managers:

Accurately assess real-world development skills.
Gain deeper insights into candidate proficiency.
Hire with greater confidence and speed.
Reduce candidate drop-off from technical friction.

For Candidates:

Enjoy a seamless, efficient assessment experience.
No need to switch between different tools or manage complex setups.
Focus purely on showcasing skills, not environment configurations.
Work in a powerful, professional-grade IDE.

Unlock a New Era of Mobile Talent Assessment

Stop guessing and start hiring the best mobile developers with confidence. Explore how HackerEarth can transform your tech recruiting.

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable or Hostinger Horizons enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

Why are Recruiters Switching to Lateral Hiring?

Explore this post with: