Cloud Engineer Expert

Cloud engineers to build the infrastructure and automation scenarios frontier AI labs use to test model judgment. AfterQuery builds the expert evaluation data that frontier AI labs use to measure and improve their models. We're hiring cloud engineers as contractors to author and grade the infrastructure and automation scenarios those evaluations run on. What you'll actually do • Author realistic cloud scenarios, from architecture design through incident response • Write reference answers with the architectural reasoning behind each decision • Grade model output for correctness, security, and cost efficiency • Flag configurations that read plausible but would fail or overspend in production Who this is for • Engineers with at least 2 years of cloud engineering on AWS, Azure, or GCP • Practitioners with hands on infrastructure as code experience such as Terraform or CloudFormation • Engineers with depth in at least one area such as networking, containers, serverless, or cost optimization • Clear writers who can document the reasoning behind an architectural decision Contract and pay • $85 to $165/hr , paid weekly via Stripe. Your rate is set from your experience, not negotiated down after you start. • Fully remote, fully async. No fixed hours. • Minimum 10 hours per week, no maximum. Scale up or down week to week. • No screen recording, no keystroke monitoring, no idle timers. • Independent contractor. Ongoing project work rather than single task batches. How our hiring process works • Apply at /apply/cloud-engineer-expert. About five minutes. Resume plus a few structured questions. • Resume review. Every application gets read against a published rubric and gets a decision. • Written assessment. You may be asked to take a written assessment for further placement. • Onboarding. Sign the contractor agreement, connect Stripe, and get platform access. • Project match. You're matched to live work in your specialty, usually within a week of onboarding. About AfterQuery AfterQuery is an expert data lab. We build the evaluation and training data that frontier AI labs use to measure what their models can and can't do in real professional work. Our contributors are practicing professionals, not annotators, and the work is credited, reviewed, and paid at professional rates.