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AI Trainer Jobs is seeking a Senior Full-Stack Engineer for a remote, multi-tenant SaaS platforms review track. You will reproduce failures, write the unit tests the model should have written, and explain the fix so the modeling team can target the gap.
Engineering model quality hinges on code that compiles, passes tests, and handles edge cases. Ideal candidates have strong full-stack experience, are proficient with unit testing, and can provide clear, concise review notes for senior engineers.
Senior Full-Stack Engineer, Multi-Tenant SaaS Platforms 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: $75–$95 / hr · Location: Remote — US-eligible · Contractor
Senior Full-Stack Engineer, Multi-Tenant SaaS Platforms is a remote engineering review track for evaluating production code, debugging traces, and developer-facing AI outputs against real-world correctness standards.
Senior Full-Stack Engineer, Multi-Tenant SaaS Platforms 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.
Track Code review & evaluation Work model Remote · Independent specialist contractor Compensation $75–$95 / hr Eligible from US
$75–$95 / hr
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.