Remote | Senior Platform Engineer — $60–$130/hour
About The Position
This is a remote, independent-contractor consulting opportunity for an experienced Senior Platform Engineer to contribute to an advanced AI training and cloud-infrastructure evaluation project. The work focuses on designing realistic infrastructure tasks and reinforcement-learning environments that assess an AI system’s ability to deploy, secure, scale, troubleshoot, and recover production-grade systems. Substantial hands-on ownership of production infrastructure and sound systems judgment are required; prior AI experience is not required. The work includes developing realistic cloud and distributed-systems scenarios, building reproducible containerized environments, creating reference implementations and defective variants, and validating environments through tests. It also involves designing security, reliability, observability, deployment, recovery, and troubleshooting scenarios; defining measurable requirements and reference solutions; and reviewing and improving benchmark tasks for realism, reproducibility, difficulty, and grading reliability. The engagement is fully remote, with displayed compensation of $60–$130 per hour. Actual compensation is output-based and paid per task that meets project specifications. Minimum weekly submission requirements apply, but the number of tasks is not specified. Selected experts are expected to begin initial tasks within approximately 24–48 hours after onboarding. Project scope and technical requirements may evolve. Work must not use confidential or proprietary information belonging to an employer, client, provider, organization, or other third party. The opportunity is available through 24-MAG LLC.
Requirements
- Senior-level experience in cloud infrastructure, platform engineering, DevOps, systems engineering, or SRE.
- Demonstrated ownership of production infrastructure or a production platform.
- Strong knowledge of distributed systems, scalable APIs, queues, autoscaling, and durable storage.
- Practical IAM, networking, and service-security experience.
- Strong observability, SLO, deployment, rollback, and disaster-recovery experience.
- Ability to write infrastructure automation or testing tools.
- Strong debugging skills in containerized environments.
- Must complete work without using confidential or proprietary information belonging to an employer, client, cloud provider, software organization, infrastructure environment, or other third party.
Nice To Haves
- Experience with Terraform or OpenTofu.
- Experience with AWS, Azure, GCP, Kubernetes, or multi-cloud infrastructure.
- Background in internal developer platforms, edge infrastructure, chaos engineering, fault injection, or resilience testing.
Responsibilities
- Design realistic tasks involving distributed systems, networking, security, scalability, and reliability.
- Evaluate architectures across compute, storage, networking, queues, APIs, and service layers.
- Incorporate production constraints, partial failures, and degraded-service conditions into scenarios.
- Define clear technical success criteria and realistic system topologies.
- Build reproducible, containerized infrastructure environments.
- Create reference implementations and intentionally defective variants.
- Develop deterministic integration, load, security, deployment, and recovery tests.
- Validate infrastructure configuration, topology, and runtime behavior.
- Support repeatable provisioning, execution, and teardown workflows.
- Design scenarios involving IAM, least privilege, private networking, and service-to-service security.
- Incorporate observability, logging, metrics, tracing, and measurable SLOs.
- Evaluate rolling deployments, rollback strategies, disaster recovery, and resilience.
- Develop troubleshooting tasks based on realistic infrastructure failures.
- Develop automation and testing utilities for environment setup and validation.
- Create reinforcement-learning environments for multi-step infrastructure reasoning.
- Define measurable requirements and golden reference solutions.
- Review peer-created tasks for ambiguity, unrealistic assumptions, and validation gaps.
- Improve reproducibility, benchmark difficulty, and grading reliability.
- Maintain strong engineering standards across project deliverables.
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What This Job Offers
Career Level
Senior
Education Level
No Education Listed
