Beyond Coding Tests: 10 Engineering Roles Mindfries Is Built to Rethink

Beyond Coding Tests: 10 Engineering Roles Mindfries Is Built to Rethink

Engineering has changed. The way we hire engineers needs to change with it.

Today, companies hire engineers to do far more than solve algorithmic problems. They investigate unfamiliar systems, reverse engineer software, build AI-powered applications, debug production incidents, and turn ambiguous customer requirements into working solutions.

Yet many technical hiring processes still rely heavily on conventional coding questions and standardized assessments.

These tools can be valuable. But no single assessment format can capture every capability required across modern engineering roles.

At Mindfries, we're building an AI interviewer focused on practical engineering skills and a broader vision for how specialized technical talent can be evaluated.

Here are ten engineering roles that demonstrate why technical assessment needs to evolve.

1. Forward Deployed Engineers (FDEs)

Forward Deployed Engineers work at the intersection of software engineering, customer problems, and real-world implementation.

They may need to integrate APIs, navigate unfamiliar codebases, build prototypes, and solve problems with incomplete requirements.

A useful assessment should explore how candidates understand a problem, investigate a system, make implementation decisions, and validate a solution.

For this role, writing code is only part of the job. Applying engineering skills in an ambiguous environment matters just as much.

2. Reverse Engineers

Reverse engineers investigate how existing software works, often without complete documentation or access to the original source code.

Their work can involve tracing program behavior, analyzing binaries, understanding protocols, and reconstructing system logic.

A conventional coding exercise may not reveal how effectively someone investigates unfamiliar software.

Role-specific assessments can instead focus on analytical reasoning, systematic investigation, and the ability to draw conclusions from limited evidence.

3. AI Research Engineers

AI research engineers turn ideas into experiments and working implementations.

Their responsibilities may include reproducing research, testing hypotheses, evaluating models, and investigating why an experiment produces unexpected results.

The important skills extend beyond writing code. They include experimental design, analytical thinking, and the ability to interpret results.

Assessments for these roles should reflect the balance between research reasoning and practical implementation.

4. Site Reliability Engineers (SREs)

When a production system fails, reliability engineers need to understand what happened, why it happened, and how to prevent it from happening again.

They investigate logs, metrics, distributed systems, performance bottlenecks, and infrastructure failures.

An effective assessment might present a realistic incident and evaluate how a candidate gathers evidence, forms hypotheses, identifies root causes, and validates a proposed fix.

The objective is to understand investigative ability not merely familiarity with terminology.

5. Security Researchers

Security researchers analyze software and systems to discover vulnerabilities, understand weaknesses, and improve security.

Their work requires careful reasoning, curiosity, and the ability to investigate unexpected behavior.

Depending on the role, relevant assessments may explore vulnerability analysis, secure coding, threat modeling, or controlled security investigations.

These assessments should be designed around authorized, safe scenarios that reflect the actual responsibilities of the position.

6. AI and LLM Engineers

Building an AI-powered application involves much more than sending a prompt to a model.

Engineers may need to integrate APIs, build retrieval systems, evaluate model outputs, manage tool calls, debug workflows, and handle unreliable responses.

A useful assessment should examine how candidates design and troubleshoot these systems, evaluate their behavior, and make practical trade-offs.

As AI applications become more common, companies need ways to evaluate the engineering skills required to build them reliably.

7. Developer Infrastructure Engineers

Developer infrastructure engineers build the tools and systems that help other engineers work efficiently.

They may maintain build systems, improve CI/CD pipelines, debug dependency problems, and develop internal tools.

Their impact often comes from solving problems that affect entire engineering teams.

Practical assessments can explore build failures, tooling improvements, automation, and the ability to understand complex development workflows.

8. Systems Engineers

Systems engineers work close to the underlying behavior of software and computing systems.

Depending on the role, they may investigate memory usage, concurrency problems, performance bottlenecks, resource management, or low-level system behavior.

These problems often require careful investigation rather than a straightforward implementation.

An assessment should reflect the technical depth required by the position and provide candidates with a meaningful opportunity to demonstrate their reasoning.

9. Data and ML Infrastructure Engineers

Data and machine learning infrastructure engineers build and maintain systems that move, transform, and serve data or support machine learning workloads.

Their work may involve pipeline failures, data quality issues, distributed processing, orchestration, and infrastructure reliability.

A candidate's ability to investigate a failed pipeline or identify an unreliable data transformation can be highly relevant to the job.

Role-specific challenges can help hiring teams evaluate practical problem-solving in these environments.

10. Solutions Engineers

Solutions engineers help translate customer requirements into technical solutions.

Depending on the organization, they may develop proof-of-concepts, integrate APIs, demonstrate product capabilities, and troubleshoot technical obstacles.

They need to understand systems, communicate clearly, and connect technical decisions to customer needs.

A relevant assessment should explore how candidates approach an unfamiliar requirement, design a solution, and explain its limitations and trade-offs.

One hiring process cannot measure everything

These ten roles share some foundational skills, but they also have important differences.

A reverse engineer and an AI researcher may both investigate unfamiliar problems, yet the tools, methods, and outcomes of their work can be very different.

Likewise, an FDE may need to balance implementation with customer requirements, while an SRE may prioritize reliability and incident response.

This is why assessment design matters.

Rather than treating every technical role as interchangeable, companies should identify the capabilities required for each position and choose evaluation methods that provide relevant evidence.

Coding challenges, technical discussions, work samples, and structured interviews can all play a role. The best combination depends on what the company needs to learn about the candidate.

Where AI-powered interviewing fits

AI creates an opportunity to make practical assessments easier to develop, administer, and evaluate.

A well-designed AI interviewing platform can help structure candidate experiences and organize evidence about problem-solving and technical work.

But AI alone doesn't guarantee a good assessment.

Tasks must be relevant to the role. Evaluation criteria must be clear. Results should be validated, and automated judgments should not replace appropriate human oversight.

Candidate privacy, accessibility, and fairness also matter.

The goal should be to improve the quality of hiring decisions—not simply to automate more interviews.

Why we're building Mindfries

Mindfries began with a simple question:

How can we better understand what an engineer is actually capable of doing?

We're building an AI interviewer focused on practical engineering skills, with the ambition to support a broader range of technical roles.

Our first version helped us turn the idea into a working product and begin testing it with early users. Now, as we move toward V2, we're working toward a more ambitious experience for candidates and hiring teams.

We want to help companies look beyond conventional interview performance and explore assessments connected to the work their engineers actually do.

For candidates, our Practice section offers a place to explore the platform and experience practical engineering challenges.

For hiring teams, Mindfries is an opportunity to explore a more role-relevant approach to technical assessment.

We're still building, learning, and improving. Feedback from engineers and employers will help shape what comes next.

The future of engineering assessment

Engineering will continue to evolve, and new technical roles will continue to emerge.

Hiring methods need to keep pace.

We believe practical, role-specific assessments can help companies gather better evidence about engineering ability, whether they're hiring software engineers, Forward Deployed Engineers, researchers, or specialists working on complex systems.

Traditional coding tests have their place. But engineering ability is bigger than a coding test.

That's the opportunity we're pursuing with Mindfries.

Candidates, explore our Practice section. Hiring teams, discover what we're building and share your feedback.

Explore Mindfries: https://mindfries.com

Mindfries — Real skills. Real evidence. Better hiring.