Software Engineer Task Author

Alignerr is building a dataset of expert tasks that train and evaluate advanced AI agents on real enterprise work. As a Task Author for the Software Engineer role, you will design and calibrate real engineering tasks — diagnosing failing integrations from logs, tracing bugs through a codebase, and writing and verifying fixes — that genuinely challenge a capable AI agent. This role requires hands-on engineering skill; generalist profiles cannot do this work.

Key Responsibilities

• Author realistic engineering task prompts: 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 drop an AI agent into 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 — adjust 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 experience in industry — 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 manufacturing, industrial IoT, or enterprise SaaS engineering contexts.
• Background with monitoring/observability platforms (e.g., Datadog, PagerDuty, Grafana).