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# Software & Firmware Engineering Experts - AI Code Evaluation

- **Company:** AuraOne Human Data
- **Location:** United States (Remote available)
- **Salary:** $208,000.0 - $249,600.0
- **Contract:** Permanent contract
- **Skills:** JavaScript (Programming Language), Test Suite, Artificial Intelligence, Unit Testing, Code Review, Software Debugging, Firmware, Junit, Open Web Application Security, Software Engineering, Rspec, Software Security, Pytest, Production Code, Free and Open-Source Software
- **Published:** October 9, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=57e6299c33ad35bf

## About the Role

* Strong day-job engineering experience - you can read, run, and debug unfamiliar code for Software & Firmware Engineering Experts - AI Code Evaluation work.
 * Comfort writing concise unit tests that capture a single failure mode.
 * Familiarity with a testing framework. pytest, Jest, or JUnit. Go test, RSpec, or whatever you use.
 * Clear written reasoning - your review note has to convince another senior engineer.
 * Reliable async availability for at least 10 hours per week.
 * Prior code-review or technical-interview-grading experience is a plus., * Open-source contributions or a public portfolio that demonstrates production-quality code.
 * Experience with the target language's standard tooling, linters, and idiomatic style guides.
 * Familiarity with security-review checklists (OWASP, CWE) and AppSec patterns., * Code review
 * Debugging
 * Unit testing
 * Software engineering judgment
 * Embedded systems

Work model

Remote - US-eligible. Remote · Independent specialist contractor. Employment type: CONTRACTOR. Applicants must be authorized to work from US.

## Description

* Run and reproduce candidate code outputs in a sandboxed environment for Software & Firmware Engineering Experts - AI Code Evaluation assignments.
 * Grade embedded systems solutions for correctness, style, and edge-case handling.
 * Write minimal failing tests that demonstrate the bug a model output missed.
 * Compare paired solutions and rank them with a written rationale tied to the rubric.
 * Tag failure modes (compile error, runtime crash, off-by-one, security issue) with severity scores.
 * Document recurring code-generation failures so the modeling team can target them.
 * Calibrate against gold-standard reviews to keep inter-rater agreement above target., * Reproduce a generated engineering solution to a coding task, run the test suite, and grade it.
 * Write the smallest failing test that demonstrates a model's edge-case bug.
 * Compare two paired solutions and rank them with a written rationale tied to the rubric.
 * Triage a security issue surfaced by a model and document the patch the model should have produced.