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, integrations, and configuration code (APIs, services, config files, CI/CD) • Ability to write and verify a code fix in a live or simulated environment • Ability to define precise, checkable correctness criteria for engineering outcomes Nice to Have • Experience in enterprise SaaS, financial technology, or large-scale distributed systems • Background with monitoring and observability platforms such as Datadog, PagerDuty, or Grafana • Familiarity with AI tools or developer platforms as an end user Why Join Us • Work on cutting-edge AI projects alongside leading research labs • Fully remote and flexible — work when and where it suits you • Freelance autonomy with the structure of meaningful, well-defined task-based work • Contribute directly to how AI systems understand and perform real software engineering • Potential for ongoing work and contract extension as new projects launch