Network Engineer - Data for Autonomous Systems annotation

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. Why this role matters 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. 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, 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. Qualifications 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 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. 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. Nice to have 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. Skills Code review Debugging Unit testing Software engineering judgment Network Engineer Data for Autonomous Systems annotation engineering review Work model Remote — US-eligible. Remote · Independent specialist contractor. Employment type: CONTRACTOR. Applicants must be authorized to work from US. Compensation $50–$70 / hr 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 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. #J-18808-Ljbffr