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.

London's engineering scene spans fintech, enterprise SaaS, and deep-tech — and this role is built for engineers who operate in that world. If you debug real systems, write infrastructure code, and understand what a production failure actually looks like, your expertise is exactly what we need.

• 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 fintech, enterprise SaaS, or regulated industry software engineering contexts
• Background with monitoring and observability platforms such as Datadog, PagerDuty, or Grafana
• Experience with microservices architectures or event-driven systems

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