Software Engineer Task Author (AI Training)

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