How We Hire the Engineers on Your Team

The process begins with a structured review of each application against the specific requirements of the role. We also review portfolio work, GitHub contributions, and project history where available, looking for evidence of real-world output rather than relying on what a resume claims.

Candidates who do not meet our baseline standards are filtered out early, keeping the process efficient and maintaining quality at every stage that follows.

This screening evaluates:

Candidates who pass the initial review move into a structured coding assessment using Coderbyte, an industry-standard platform that supports multiple programming languages and a broad library of technical challenges. This stage gives us objective, measurable data on how a candidate actually approaches a coding problem, not just whether they arrive at the right answer.

Importantly, AI use is not flagged as a problem at this stage. We expect candidates to use AI tools. Our assessments are configured to include prompt engineering challenges that require candidates to work with AI effectively as part of the evaluation itself. What we are assessing is not whether a candidate reached for AI, but how well they used it.

Candidates who do not demonstrate strong technical competency at this stage are not advanced.

Beyond correctness, we evaluate:

Our technical interviews go beyond theory. Passing the coding assessment earns a candidate a structured interview with Chromedia's engineering team, where we explore how they think and communicate under real conditions, not just whether they know the right answers.

Where applicable, interviews are tailored to match your project's specific tech stack and architecture requirements, ensuring the candidate is not just technically strong in general but the right fit for what your team is actually building.

This interview also sets up the conversation that follows. Once we have a clear picture of how a candidate thinks through engineering problems, we move directly into evaluating how they work alongside AI, because in modern software development, those two things are inseparable.

We explore how candidates:

Automated assessments can tell us a lot about a candidate's technical ability, but they cannot yet tell us whether a candidate uses AI the way a professional engineer should. That evaluation requires a human conversation, and it happens here.

This stage is a structured interview focused specifically on how a candidate works with AI tools in practice. We are looking for fluency, not just familiarity. There is a meaningful difference between a developer who understands how to construct clear, well-scoped prompts that return useful output on the first or second attempt, and one who iterates inefficiently through vague instructions, burning through tokens and time to arrive at a result a better prompt would have produced immediately.

This is one of the most important evaluations in our hiring process. As AI tooling becomes central to how software gets built, the developers who use it well will consistently outperform those who do not, and the gap between them will only grow. We hire for that capability intentionally.

We assess candidates across four dimensions:

Technical ability gets an engineer through the door. Character is what determines whether they thrive on a long-term client team.

This stage is a structured conversation designed to assess professional character and cultural fit. We evaluate candidates across five dimensions that our experience shows are consistently predictive of long-term performance.

We pay particular attention to how candidates handle direct feedback and technical disagreements. Engineers who can push back thoughtfully, defend a position with evidence, and accept a better argument gracefully are the ones who make teams stronger over time. That quality is rare, and we hire for it deliberately.

For senior roles and certain client engagements, candidates are given a take-home project. This stage reveals things that a structured interview cannot: how a candidate organizes their work, what code design principles they apply without being directed to, how they document their decisions, and whether they deliver more than what was asked.

AI use is fully permitted during the take-home. In fact, how a candidate chooses to use it is part of what we are evaluating. We look at whether they applied AI tools thoughtfully to accelerate the right parts of the work, and whether the output reflects genuine engineering judgment or an over-reliance on generated code that has not been properly reviewed or adapted.

The results consistently distinguish mid-level developers from senior ones, regardless of what a resume claims. We classify candidates based on what we observe, not on self-reported years of experience. A candidate who claims senior-level status but produces mid-level work is assessed accordingly.

Before any candidate is placed on a client team, the recruiting team conducts a final structured review of the full assessment record. This is a deliberate conversation about the candidate's strengths, areas for development, and fit for the specific engagement.

Candidates who meet or exceed our standards across every phase are extended an offer. Those who fall short at any stage are not advanced, regardless of how well they performed elsewhere in the process. Every engineer we place has cleared every phase of this evaluation.

When a candidate meets or exceeds our expectations, we move forward with an offer and begin onboarding. Contracts, documentation, and role expectations are prepared and reviewed thoroughly before anyone starts. We do not rush this step, because a well-onboarded engineer contributes faster and integrates more smoothly into a client team from day one.

By the time a Chromedia engineer reaches your project, the hard work of finding, testing, and verifying them is already done. What you receive is someone who has demonstrated technical ability, sound engineering judgment, clear communication, and the capacity to work effectively with modern AI tools. That foundation is what allows our teams to build genuine product expertise over time and deliver the kind of consistent, high-quality output that makes a real difference to your product.