Senior Backend Engineer for AI Model Training

Design and build realistic Reinforcement Learning environments that train and evaluate AI models on production-grade cloud infrastructure tasks. Your work will create reproducible scenarios that test AI systems on design, deployment, troubleshooting, security, scaling, and recovery of distributed systems.

• Design, develop, and implement cloud infrastructure environments that evaluate AI model proficiency in systems design, deployment, and troubleshooting.
• Create detailed, reproducible scenarios covering distributed systems, networking, Identity and Access Management (IAM), message queues, persistent storage, observability, rolling deployments, and disaster recovery.
• Develop deterministic validation tests and golden reference solutions to verify environment behavior and model correctness.
• Produce intentionally defective variants and failure scenarios to test AI model responses and recovery strategies.
• Document architecture, edge cases, and operational flows for each environment to ensure clarity and reproducibility.
• Collaborate with technical leads and project participants to refine environment specifications and acceptance criteria iteratively.
• Apply DevOps and infrastructure automation practices to deliver scalable, secure, and maintainable evaluation environments.

• Proven expertise in backend programming using one or more of these languages: C++, Python, Rust, GoLang, JAVA, JavaScript.
• Strong practical experience with DevOps, cloud infrastructure, CI/CD pipelines, and automation tools.
• Demonstrated ability to architect, scale, and secure distributed systems in production-grade environments.
• Deep understanding of networking, IAM, message queues, durable storage, observability, rolling deployments, and disaster recovery concepts.

• Role type: Contractor.
• Commitment: 20 hours per week.
• Location: Remote.
• Project context: Engagement is for an advanced, customer-facing project focused on creating Reinforcement Learning Environments for AI model training and evaluation.
• Start timeline: Roles are typically filled within 48 hours; candidates who can jump in right away are preferred.

• Pay rate: $50 - $100 per hour.
• Payment model: Output-based, paid per task that meets the project specifications.
• Work duration per task will vary by expert experience and workflow.
• Minimum submission requirements apply. Experts must submit a minimum of tasks per week.

• Applicants must be able to engage as independent contractors.

• Apply to the role and complete the screening questions.
• Complete an AI interview, approximately 30 minutes, which is reviewed by recruiters.
• Final review by the hiring manager.