MNTN Reimagines Technical Hiring for AI-Enhanced Engineering

MNTN, the Hardest Working Software in Television and a trailblazer in Performance TV advertising, is experiencing rapid growth. As they scale, they’ve recognized a fundamental shift in how software development happens in 2025 – engineers increasingly collaborate with AI tools as part of their daily workflow. The rise of AI-assisted development is creating a new class of highly productive engineers who can accomplish significantly more with the same time investment. Organizations that don’t adapt their hiring and development practices to identify and cultivate these skills will find themselves at a competitive disadvantage. Rather than treating AI as a future consideration, MNTN has made it a present-day requirement for engineering excellence. An engineering leader puts it directly: “If a candidate can’t explain how they’re using AI in their work, it’s a hard no.”

MNTN wanted to assess these skills, but traditional interview platforms either banned AI use entirely or provided no way to observe how candidates use AI to solve realistic problems. MNTN needed CoderPad.

CoderPad’s AI-Native Interview Platform

MNTN’s bold stance to make AI proficiency essential, not optional, reflects a larger transformation in the industry. Just as previous generations of developers had to master version control, package managers, and IDEs, today’s engineers need fluency with AI assistants to remain competitive. 

CoderPad provided MNTN with the tools to transform their hiring philosophy into a practical, measurable process. CoderPad’s Screen and Interview products feature fully collaborative coding environments to ask real-world questions. Often, these are questions that involve solving problems with the help of AI. 

CoderPad takes it a step further with a fully integrated, collaborative AI Assistant feature that candidates and interviewers can use as they solve a problem together within an interview. CoderPad’s in-product assistant, which features state-of-the-art models from OpenAI, Anthropic, Google, and Meta, makes collaborating with AI a natural part of the workflow and turns this collaboration into a legitimate, observable part of the technical assessment.

AI Fluency as a Top-of-Funnel Filter

MNTN evaluates AI proficiency throughout their interview process, starting with the hiring manager screen, where candidates are asked pointed questions about their AI experience:

• “Describe a complex challenge you solved using AI tools.”
• “How have you experimented with automation using AI?”

This isn’t about checking a box for AI familiarity. MNTN seeks candidates who are genuinely AI-native. Candidates must articulate specific examples that demonstrate AI as an integral part of how they approach engineering challenges. 

Those candidates who can’t provide concrete, non-trivial examples of AI integration don’t proceed. 

Technical Interviews That Embrace AI Collaboration

For those candidates who proceed to the MNTN’s virtual on-site stage, MNTN leverages CoderPad Interview’s embedded AI Assist feature to create something traditional platforms don’t offer: AI-enabled technical assessments with complete transparency and reviewable history. The interviewing team sets candidate expectations around the use of CoderPad Interview’s AI Assistant by instructing candidates: 

You can use an AI assistant as a tool during this interview, just like in a real-world setting. That said, we’d like to first see your initial thought process and approach before turning to AI. Feel free to use it if you get stuck or need help refining your solution. Our goal is to understand how you think through problems and how you leverage tools like AI to enhance, not replace, your problem-solving.

CoderPad code interview all

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The team emphasizes to candidates that AI isn’t a crutch, it’s a multiplier and the reality of modern engineering. 

With CoderPad Interview’s AI Assistant, interviewers can see in real-time:

• What AI prompts a candidate crafts during the interview
• How they manage context through the session
• Whether they critically evaluate AI-generated code
• How they integrate AI outputs into their solution

MNTN designs their interviews to be difficult to complete within the time constraint without effectively leveraging AI assistance – mirroring the complexity of real-world engineering tasks. CoderPad’s collaborative multi-file environment and realistic application structure make this possible. CoderPad’s transparency and control features make it possible for MNTN to encourage AI use while still maintaining rigorous evaluation standards.

Core AI Skills MNTN Evaluates in their Process

CoderPad’s AI-enabled platform allows MNTN to assess specific skills that distinguish thoughtful AI collaborators from simple copy-and-paste users. 

• Strategic Problem Decomposition: CoderPad’s multi-file environment, progressive instructions, and Drawing Mode reveal how candidates break down complex requirements into manageable pieces that can be effectively delegated to AI or handled directly.
• Effective Prompt Engineering: The AI Assistant’s transparency and real-time collaboration show interviewers the exact prompts candidates craft as they type in each keystroke, demonstrating their skill in eliciting useful outputs from AI assistants.
• Context Management: CoderPad’s ability to run multiple contexts in parallel and session recording captures how candidates maintain appropriate context throughout an AI collaboration session, preventing drift and ensuring continuity.
• Technical Discernment: The platform’s real-time code execution and testing capabilities allow interviewers to observe candidates evaluate AI-generated code for correctness, efficiency, and adherence to best practices.
• Integration Expertise: CoderPad’s realistic development environment, with both AI Assistants and Terminal-based Agents, shows how candidates critically evaluate and seamlessly integrate AI outputs while maintaining code quality and architectural standards.

Candidates who excel across these dimensions show they’re ready to thrive in an AI-augmented engineering environment. CoderPad’s AI Assistant with file context and multiple models, session transparency, multi-file environments, and real-time collaboration gives MNTN a powerful platform to scale and accelerate their AI-centric technical interview process.

Measurable Impact: AI-Forward Results Enabled by CoderPad 

MNTN’s AI-centric process, enabled by CoderPad’s Technical Hiring Platform, has delivered quantifiable improvement that extends far beyond hiring quality:

• Lightning-fast onboarding: New engineers make meaningful contributions in 1-2 days (down from weeks). These engineers apply the same AI collaboration skills they demonstrated in CoderPad interviews to rapidly onboard and shorten their time to impact.
• Early delivery: Features are completed ahead of schedule by developers comfortable with AI-augmented development.
• Workflow acceleration: Critical tasks that previously took days are now completed in hours
• Superior talent: Engineers hired through the CoderPad AI-enabled process consistently outperform previous cohorts at the 90-day mark across key dimensions: they work faster, produce higher quality code, and adapt more quickly to new challenges.

By using CoderPad to prioritize AI fluency in hiring, MNTN has built a team culture centered on AI collaboration. This AI-first mindset has transformed team velocity. MNTN’s engineering teams now deliver features weeks ahead of schedule while simultaneously addressing longstanding technical debt, a combination previously impossible within their resource constraints.

Conclusion: CoderPad Transforms Technical Hiring for the AI Era

AI has fundamentally changed how software gets built in 2025, making human-AI collaboration the standard rather than the exception. MNTN recognized this shift, but needed a partner to turn their hiring philosophy into measurable evaluation criteria. 

CoderPad’s AI-native Technical Hiring interview Platform provided the solution: a way to transparently assess AI collaboration skills across the entire recruitment funnel. The results speak for themselves: faster onboarding, accelerated delivery, and superior 90-day performance from engineers who demonstrated their AI proficiency through CoderPad’s platform in the interview process.

Organizations that adapt their hiring practices to identify and cultivate AI collaboration skills will maintain a competitive advantage. CoderPad provides the infrastructure to make that adaptation possible.

With CoderPad, MNTN has proven that AI-first hiring isn’t theoretical; it’s measurable, repeatable, and a clear competitive advantage.