Software Engineer Task Author (AI Training)

Alignerr; Greater London; Remote; GBP 55,000 - 110,000; Part time

Benefits offered by this job

Fully remote

Freelance autonomy

Meaningful, well-defined task-based工作

Job summary

Alignerr is seeking a skilled Task Author to design authentic engineering challenges for evaluating AI agents. You will diagnose failures from logs, trace bugs in codebases, and write real fixes, contributing to robust evaluation of enterprise-grade software tasks.

Based in a fully remote setting, you will work on flexible, task-based assignments with fast feedback loops. The role suits engineers who debug real systems and understand production failures in fintech, enterprise SaaS, or related

Qualifications

• Hands-on, industry-level softwareengineering experience.
• Fluency with Git, version control workflows, and code reviews.

Responsibilities

• Author realistic engineering task prompts covering failures, logs, and fixes.
• Write scoring rubrics and verification criteria for fixes and diagnoses.
• Set up task environments with codebases, logs, and system states for AI agents.
• Solve tasks by coding fixes and validating them end-to-end.
• Calibrate task difficulty by adjusting code complexity and logs.
• Review and correct AI-drafted prompts and rubrics when provided.

Skills

Git proficiency

Debugging

Code review

Observability

CI/CD

Tools

Git

CI/CD

Observability tools

Code review

Job description

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.

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 engineeringPotential for ongoing work and contract extension as new projects launch