In this role, you'll apply your platform engineering expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world engineering input. No prior experience in AI is required-your domain knowledge and hands-on production experience are what matter.
As an expert, you will create Reinforcement Learning Environments that test an AI model’s ability to design, deploy, troubleshoot, secure, scale, and recover production-grade cloud infrastructure. You will develop realistic scenarios involving distributed systems, networking, IAM, queues, durable storage, observability, rolling deployments, and disaster recovery, then build reproducible environments, deterministic validation tests, golden reference solutions, and intentionally defective variants.
• Debug environments, document technical decisions, and review tasks created by other experts.
Required Skills and Qualifications
• Senior-level technical architecture, cloud infrastructure, platform engineering, DevOps, systems engineering, or SRE experience, including personal ownership of a production platform.
• Strong knowledge of distributed systems, scalable APIs, queues, autoscaling, durable storage, and partial-failure scenarios.
• Practical experience with IAM, private networking, least-privilege access, and service-to-service security.
• Experience with observability, measurable SLOs, rolling deployments, rollback strategies, and disaster recovery.
• Ability to write infrastructure automation or testing tools and debug containerized environments using a relevant programming language.
Preferred Qualifications
• Experience with Terraform or OpenTofu.
• Experience with AWS, Azure, GCP, Kubernetes, or multi-cloud infrastructure.
• Experience building internal developer platforms, edge infrastructure, or shared platform services.
• Experience with chaos engineering, fault injection, local cloud emulators, or resilience testing.
• Experience creating technical evaluations, automated grading systems, or AI environments is helpful but not required.
Process:
• Apply to the role, filling out the screening questions
• Complete AI interview (aprox. 30 minutes), reviewed by recruiters)
• Hiring Manager review
Compensation Structure
Compensation is output-based; experts are paid per task that meets the project specifications. The time required to complete work may vary depending on the expert’s experience and workflow. Minimum submission requirements apply. Experts must submit a minimum of tasks per week.
Start Timeline & Availability
We typically fill roles within 48 hours and are looking for experts ready to jump in right away. If selected, we expect you to start your first tasks within 24–48 hours of completing onboarding.
Skills mentioned
Cloud Benchmark Task AuthoringCloud and Distributed Systems ArchitectureProduction Infrastructure Ownership
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