Network Engineer - Data for Autonomous Systems annotation

AI Trainer Jobs; United States; Remote; USD 69,000 - 96,000; Full time

Job summary

AI Trainer Jobs is seeking a Network Engineer to evaluate autonomous systems annotation code in a remote, contractor role. You will run and reproduce candidate code outputs, grade solutions for correctness and style, and write minimal failing tests to demonstrate bugs.

You will compare paired solutions and provide rationale aligned with rubric. The role emphasizes strong debugging skills, concise review notes, and experience with testing frameworks.

Qualifications

• Strong day-job engineering experience reading, running, and debugging unfamiliar code.
• Ability to write concise unit tests capturing a single failure mode.
• Familiarity with a testing framework (pytest, Jest, JUnit, Go test, RSpec, or equivalents).

Responsibilities

• Run and reproduce candidate code outputs in a sandboxed environment for the annotation assignments.
• Grade network engineer data for autonomous systems annotation engineering review solutions for correctness, style, and edge-case handling.
• Write the smallest failing test that demonstrates the bug.
• Compare paired solutions and rank them with a written rationale tied to the rubric.

Skills

Code review

Debugging

Unit testing

Software judgment

Network engineering

Tools

pytest

Jest

JUnit

Go test

RSpec

Job description

Network Engineer - Data for Autonomous Systems annotation is a remote engineering review track for evaluating production code, debugging traces, and developer-facing AI outputs against real-world correctness standards. Reviewers reproduce failures, write the unit test the model should have written, and explain the fix so the modeling team can target the gap.

Category: Coding, SWE & Agent Evaluation · Pay: $50–$70 / hr · Location: Remote — US-eligible · Contractor

About the role

Network Engineer - Data for Autonomous Systems annotation is a remote engineering review track for evaluating production code, debugging traces, and developer-facing AI outputs against real-world correctness standards. Reviewers reproduce failures, write the unit test the model should have written, and explain the fix so the modeling team can target the gap.
Engineering model quality lives or dies on whether the generated code actually compiles, passes tests, and handles edge cases. AuraOne pairs experienced engineers with the modeling team to grade outputs the way a code reviewer would.
Judge generated code and software engineering agents. Read their debugging traces.

Responsibilities

• Run and reproduce candidate code outputs in a sandboxed environment for Network Engineer - Data for Autonomous Systems annotation assignments.
• Grade network engineer data for autonomous systems annotation engineering review 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.

Role details

Track Code review & evaluation Work model Remote · Independent specialist contractor Compensation $50–$70 / hr Eligible from US

What you should bring

• Strong day-job engineering experience — you can read, run, and debug unfamiliar code for Network Engineer - Data for Autonomous Systems annotation 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.

Example tasks

• 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.

Useful experience

• 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.

Compensation and schedule

$50–$70 / hr
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

Skills used in matching

• Code review
• Debugging
• Unit testing
• Software engineering judgment
• Network Engineer Data for Autonomous Systems annotation engineering review