Network Engineer - Talent Network (Data for Autonomous Systems)

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

Job summary

AuraOne is seeking a Network Engineer for the Talent Network to evaluate production code, debug traces, and AI outputs against correctness standards. Reviewers reproduce failures, write unit tests, and explain fixes to guide model improvements.

Work is remote, contractor-based with US eligibility, and compensation is $50–$70 per hour. The role emphasizes code review, testing, and precise reasoning for evaluation tasks.

Qualifications

• Strong day-job engineering experience — read, run, and debug unfamiliar code.
• Comfort writing concise unit tests that capture a single failure mode.
• Familiarity with a testing framework: pytest, Jest, JUnit, Go test, RSpec, or equivalent.
• Clear written reasoning — review notes must convince a senior engineer.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Run and reproduce candidate code outputs in a sandboxed environment.
• Grade network engineer 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 rubric-based rationale.

Skills

Engineering experience

Unit tests

Testing frameworks

Written reasoning

Availability 10h/wk

Code-review experience

Job description

Network Engineer - Talent Network (Data for Autonomous Systems) 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

Network Engineer - Talent Network (Data for Autonomous Systems) is a remote engineering review track for evaluating production code, debugging traces, and developer-facing AI outputs against real-world correctness standards.

About the role

Network Engineer - Talent Network (Data for Autonomous Systems) 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 - Talent Network (Data for Autonomous Systems) assignments.
• Grade network engineer 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 - Talent Network (Data for Autonomous Systems) 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 engineering review

Application boundary

Creating a specialist profile records your experience and preferences. Starting role intake is a separate action that attaches this role to your candidate record.

Specialist intake

The intake preserves your chosen role, the visible terms, and source attribution for reviewer context.

• 01 Confirm profile and eligibility details.
• 02 Attach this role deliberately.
• 03 Receive a human review decision or follow-up.
• Placement timing depends on program demand and reviewer confirmation.