Senior Platform Engineer for AI Training

Design and build realistic cloud infrastructure scenarios that train and evaluate next-generation AI systems. You will create reproducible, production-grade Reinforcement Learning environments that exercise an AI model''s ability to design, deploy, secure, scale, observe, and recover distributed systems. This is a remote contractor role focused on platform and infrastructure expertise rather than prior AI experience.

• Create realistic cloud infrastructure tasks that cover distributed systems, networking, security, scalability, and reliability.
• Develop reproducible, containerized environments with valid golden reference solutions and intentionally defective variants for evaluation and training.
• Define measurable requirements for infrastructure configuration, deployed topology, and runtime behavior.
• Author deterministic integration, load, security, failure-injection, deployment, and recovery tests.
• Debug environments, document technical decisions, and review tasks authored by other experts.

• Senior-level experience in cloud infrastructure, platform engineering, DevOps, systems engineering, or SRE, 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 models, 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 to debug containerized environments using a relevant programming language.
• Required skills called out include cloud benchmark task authoring, cloud and distributed systems architecture, and production infrastructure ownership.

• Experience with Terraform or OpenTofu.
• Familiarity with AWS, Azure, GCP, Kubernetes, or multi-cloud environments.
• Background 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.

• Role type, contractor. Remote work.
• Compensation is output-based, paid per task that meets project specifications.
• Hourly pay range referenced: 60 to 130 USD per hour. Actual payments are task-based and depend on the project and task scope.
• The time required to complete work may vary by expert and workflow. Minimum submission requirements apply. Experts must submit a minimum number of tasks per week; the exact minimum is set by the project.

• Rate guidance: 60 to 130 USD per hour.
• Payment structure: paid per completed task that satisfies project specifications, rather than fixed salary. Payment frequency and exact per-task amounts are determined by the project.

• Apply and complete screening questions as part of your application.
• Complete an AI interview, approximately 30 minutes in length, which is reviewed by recruiters.
• Application materials and interview results proceed to Hiring Manager review.

• This role is offered as an independent contractor engagement. Candidates must be available to engage under contractor terms.
• No prior AI experience is required; domain expertise and hands-on production platform experience are the primary requirements.