AI/ML Code Reviewer & Quality Engineer (Remote)

AuraOne, Inc.; United States; Remote; USD 83,000 - 165,000; Full time

Job summary

AuraOne, Inc. is seeking an AI/ML Engineer for a remote engineering review track. You will evaluate production code, debug traces, and AI outputs against real-world correctness standards, reproducing failures and explaining fixes so the modeling team can target gaps.

Responsibilities include running code in sandboxed environments, grading solutions for correctness and edge cases, writing minimal failing tests, and documenting recurring failures to improve future reviews.

Qualifications

• Must have ability read, run, and debug unfamiliar code for AI/ML Engineer work.
• Experience writing concise unit tests that capture a single failure mode.
• Familiarity with testing frameworks: 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.

Responsibilities

• Run and reproduce candidate code outputs in a sandboxed environment for AI/ML Engineer assignments.
• Grade ML 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.
• Document recurring code‑generation failures so the modeling team can target them.
• Calibrate against gold‑standard reviews to keep inter‑rater agreement above target.

Skills

Code review

Debugging

Unit testing

Software engineering judgment

ML engineering

AI model evaluation

Tools

pytest

Jest

JUnit

Go test

RSpec

Job description

AuraOne, Inc. is seeking an AI/ML Engineer for a remote engineering review track. You will evaluate production code, debug traces, and AI outputs against real-world correctness standards, reproducing failures and explaining fixes so the modeling team can target gaps.

Responsibilities include running code in sandboxed environments, grading solutions for correctness and edge cases, writing minimal failing tests, and documenting recurring failures to improve future reviews.