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# Software Engineer (AI-Native Platform & Integrations)

- **Company:** Human Union Data, Inc.
- **Location:** United States (Remote available)
- **Salary:** $145,600.0 - $228,800.0
- **Contract:** Permanent contract
- **Skills:** JavaScript (Programming Language), Test Suite, Artificial Intelligence, Unit Testing, Cloud Computing, Code Review, Software Debugging, Junit, Open Web Application Security, Software Engineering, Systems Integration, Rspec, Software Security, Pytest, Free and Open-Source Software, Golang
- **Published:** July 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=b2147f26078b29a7

## About the Role

* Strong day-job engineering experience - you can read, run, and debug unfamiliar code for Software Engineer (AI-Native Platform & Integrations) work.
 * Comfort writing concise unit tests that capture a single failure mode.
 * Familiarity with at least one of: pytest, Jest, JUnit, Go test, RSpec, or equivalent.
 * 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
 * Cloud & platform engineering

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 Engineer (AI-Native Platform & Integrations) assignments.
 * Grade cloud & platform engineering solutions for correctness, complexity, idiomatic 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.


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