Remote ML Engineer for AI Model Evaluation & Testing

AuraOne; United States; Remote; USD 152,000 - 207,000; Part time

Job summary

AuraOne is seeking an ML Engineer (MSc/PhD) for AI Model Evaluation in a remote, US-eligible contractor role. You will reproduce failures, write unit tests the model should have written, and explain fixes to target gaps.

You will grade ML solutions for correctness and edge cases, document failures with severity, and craft minimal tests. Strong coding and clear reasoning are essential; 10+ hours/week availability is required.

Qualifications

• Strong day-job engineering experience for ML Engineer — AI Model Evaluation 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 equivalents.
• Clear written reasoning — your review note has to convince a 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 ML Engineer (MSc/PhD) — AI Model Evaluation 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

Education

MSc/PhD in a relevant field

Tools

pytest

Jest

JUnit

Go test

RSpec

Job description

AuraOne is seeking an ML Engineer (MSc/PhD) for AI Model Evaluation in a remote, US-eligible contractor role. You will reproduce failures, write unit tests the model should have written, and explain fixes to target gaps.

You will grade ML solutions for correctness and edge cases, document failures with severity, and craft minimal tests. Strong coding and clear reasoning are essential; 10+ hours/week availability is required.