Data Engineering Expert

AI Trainer Jobs; United States; Remote; USD 124,000 - 172,000; Full time

Benefits offered by this job

Remote work

Job summary

AI Trainer Jobs is seeking a Data Engineering Expert contractor for a remote engineering review track. You will evaluate production code, reproduce failures, write unit tests the model should have written, and explain fixes so the modeling team can target the gap.

Engineering model quality hinges on code that compiles, passes tests, and handles edge cases. You will be paired with experienced engineers to grade outputs as a code reviewer would, read debugging traces, and provide concise,

Qualifications

• Open-source experience or portfolio demonstrating production-quality code.
• Comfort with a testing framework (pytest, Jest, JUnit, Go test, RSpec or equivalent).
• Ability to read, run, and debug unfamiliar code for evaluation tasks.
• Ability to produce concise, persuasive review notes.

Responsibilities

• Run and reproduce candidate outputs in a sandbox for assignments.
• Grade data engineering solutions for correctness, style, and edge-case handling.
• Write minimal failing tests that demonstrate a bug.
• Compare paired solutions and provide rationale aligned with rubric.
• Tag failure modes with severity scores and document patches.

Skills

Data engineering

Code reading

Debugging

Unit testing

Clear written reasoning

Async availability

Tools

pytest

Jest

JUnit

Job description

Data Engineering Expert 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: $90–$125 / hr · Location: Remote — US-eligible · Contractor

Data Engineering Expert is a remote engineering review track for evaluating production code, debugging traces, and developer-facing AI outputs against real-world correctness standards.

Role details

About the role

Data Engineering Expert 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 Data Engineering Expert assignments.
• Grade data engineering 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.Track Code review & evaluation Work model Remote · Independent specialist contractor Compensation $90–$125 / hr Eligible from US

What you should bring

• Strong day-job engineering experience — you can read, run, and debug unfamiliar code for Data Engineering Expert 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

$90–$125 / 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
• Data engineering