Software Engineer Task Author (AI Training)

Software Engineer Task Author (AI Training) About the Role Alignerr is building a dataset of expert tasks that train and evaluate advanced AI agents on real enterprise engineering work. As a Task Author, you will design and calibrate authentic engineering challenges — diagnosing failing integrations from logs, tracing bugs through codebases, and writing and verifying real fixes — that genuinely push the limits of capable AI agents. This is hands-on engineering work. Generalist or theoretical profiles cannot do this. If you're a working software engineer who debugs real systems for a living, this is a rare opportunity to shape how the next generation of AI understands engineering. Organization : Alignerr Type : Hourly Contract Location : Remote Commitment : Flexible, task-based Key Responsibilities Author realistic engineering task prompts covering integration failures, log-based debugging, bug traces through source code, and configuration or integration fixes Write scoring rubrics that define exactly what a correct fix or diagnosis looks like and how it is verified — including live environment validation where applicable Set up task environments with realistic codebases, logs, alerts, and system states that place an AI agent in a plausible engineering situation Solve each task yourself — write the actual fix and confirm it works — to validate the task is sound and the rubric is accurate Calibrate task difficulty by adjusting code complexity, log volume, failure modes, or ambiguity until the task reliably challenges the model to the intended degree Review and correct AI-drafted task prompts or rubrics when provided Qualifications Working software engineer with hands-on, industry-level experience — this is not a theoretical or instructional role Fluency in Git, version control workflows, and code review practices Strong debugging skills: comfortable reading logs, using observability tools, and tracing failures through a codebase Experience with infrastructure, integra