In this role, you'll apply your cloud infrastructure and 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.
Responsibilities
- Create realistic cloud infrastructure tasks involving distributed systems, networking, security, scalability, and reliability.
- Build reproducible, containerized environments with valid reference solutions and intentionally defective variants.
- Define measurable requirements across infrastructure configuration, deployed topology, and runtime behavior.
- Develop deterministic integration, load, security, failure-injection, deployment, and recovery tests.
- Debug environments, document technical decisions, and review tasks created by other experts.
Requirements
- Senior-level 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.
Nice to have
- 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.
Benefits
- Compensation is output-based; experts are paid per task that meets the project specifications.
Additional details
- Job Title: Senior Platform Engineer
- Job Type: Contractor
- Location: Remote
- About the hiring company: Our client is a rapidly growing, venture-backed AI company helping shape the next generation of intelligent systems. By combining world-class human expertise with advanced machine learning workflows, they enable leading AI organizations to build, evaluate, and improve cutting-edge models used across a wide range of industries. The company works with highly accomplished professionals in fields such as software engineering, finance, healthcare, legal, operations, research, and other specialized domains. These experts contribute directly to the development of advanced AI systems by providing real-world knowledge, evaluations, feedback, and domain-specific judgment that help models reason more accurately and perform more effectively. Leveraging a proprietary AI-driven talent assessment and matching platform, the organization identifies exceptional professionals globally and connects them with high-impact projects at the forefront of artificial intelligence. Backed by more than $40 million in funding and supported by a rapidly expanding international network of experts, the company is building critical human intelligence infrastructure for the AI economy and creating meaningful opportunities for professionals to apply their expertise in entirely new ways.
- Process: Apply to the role, filling out the screening questions. Complete AI interview (approx. 30 minutes), reviewed by recruiters. Hiring Manager review.
- 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.
- Originally posted on Himalayas
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