Senior Technical Architect for AI Model Training

Role Overview

Design and author realistic cloud infrastructure challenges used to train and evaluate next-generation AI systems. You will convert production platform engineering expertise into reproducible, containerized environments, deterministic tests, golden reference solutions, and intentionally defective variants that exercise an AI model''s ability to design, deploy, secure, scale, and recover production-grade cloud systems. No prior AI experience is required, your domain expertise and hands-on production ownership matter.

Key Responsibilities

• Create realistic cloud infrastructure tasks that cover distributed systems, networking, security, scalability, reliability, and partial-failure scenarios.
• Build reproducible, containerized environments with valid reference solutions and intentionally defective variants for evaluation.
• Define measurable requirements covering infrastructure configuration, deployed topology, and runtime behavior.
• Develop deterministic tests, including integration, load, security, failure-injection, deployment, and recovery tests.
• Author benchmark tasks for cloud platform evaluation and verification.
• Debug environments, document technical decisions and assumptions, and review or provide feedback on tasks created by other experts.

Qualifications

• Senior-level experience in technical architecture, cloud infrastructure, platform engineering, DevOps, systems engineering, or SRE, including personal ownership of a production platform.
• Strong knowledge of distributed systems, scalable APIs, queuing systems, autoscaling, durable storage, and handling partial-failure scenarios.
• Practical experience with IAM, private networking, least-privilege access models, and service-to-service security.
• Experience with observability practices, measurable SLOs, rolling deployments, rollback strategies, and disaster recovery planning.
• Ability to author infrastructure automation or testing tools, and to debug containerized environments using a relevant programming language.
• Preferred: experience with Terraform or OpenTofu, AWS, Azure, GCP, Kubernetes, multi-cloud setups, internal developer platforms, edge infrastructure, chaos engineering or fault injection, and resilience testing.
• Preferred but not required: prior experience creating technical evaluations, automated grading systems, or AI evaluation environments.

Work Terms

• Role type, contractor, remote.
• Work focuses on producing tasks and evaluation artifacts, with ownership of the environments and tests you create.
• Compensation is output-based, experts are paid per task that meets project specifications. Time to complete a task will vary with experience and workflow.
• Minimum submission requirements apply. Experts must submit a minimum number of tasks per week.

Compensation

Pay range: $60 to $130 per hour. Actual payment is task-based, paid per completed task that meets the project specifications.

Application Process

• Apply to the role and complete the screening questions.
• Complete an AI interview, approximately 30 minutes, which will be reviewed by recruiters.
• Candidates who pass initial screening will receive a hiring manager review.

Eligibility

• Must be able to work as an independent contractor and perform remote work.
• No prior AI experience required; applicants must bring production platform engineering expertise.