ML Engineer (MSc/PhD) — AI Model Evaluation

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

Category: Coding, SWE & Agent Evaluation · Pay: $130 / hr · Location: Remote — US-eligible · Contractor

ML Engineer (MSc/PhD) — AI Model Evaluation 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 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.

Qualifications

• Strong day-job engineering experience — you can read, run, and debug unfamiliar code for ML Engineer (MSc/PhD) — 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 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.

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
• ML engineering
• AI model evaluation

Work model

Remote — US-eligible. Remote · Independent specialist contractor. Employment type: CONTRACTOR. Applicants must be authorized to work from US.

Compensation

Hourly rate confirmed after the interview process.