Senior Backend Engineer for AI Model Training [Remote]

Role Overview

Build realistic, production-grade cloud infrastructure environments used to train and evaluate AI models through reinforcement learning. You will design, implement, and validate scenarios that test an AI model''s ability to design, deploy, troubleshoot, secure, scale, and recover distributed systems in real-world conditions. This role focuses on creating reproducible environments, deterministic validation, golden reference solutions, and intentionally defective variants to push model performance on infrastructure tasks.

Key Responsibilities

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

Qualifications

• Senior backend engineering experience, demonstrated by past work designing, building, or operating production distributed systems.
• Proven expertise with one or more backend programming languages such as C++, Python, Rust, GoLang, Java, or 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, queues, durable storage, observability, and disaster recovery concepts.
• No prior AI experience required, domain and infrastructure expertise are the primary requirements.

Work Terms

• Role type, contractor engagement.
• Part-time commitment, approximately 20 hours per week.
• Position is fully remote.
• Work is output-focused, you will be expected to deliver tasks that meet project specifications.

Compensation

• Rate range: $50 to $100 per hour.
• Compensation structure is output-based, experts are paid per task that meets the project specifications.
• Time required to complete work may vary by expert experience and workflow.
• Minimum submission requirements apply, experts must submit a minimum number of tasks per week.

Application Process

• Submit an application and complete screening questions.
• Complete an AI interview, approximately 30 minutes, which will be reviewed by recruiters.
• Selected candidates will undergo a hiring manager review.

Project Availability and Minimum Commitments

• Placement on the project depends on current project availability and selection outcomes.
• Minimum submission requirements apply, experts must submit a minimum number of tasks per week.

About the Organization

The organization is an AI data lab that transforms domain expertise into high-quality training data, evaluations, and feedback loops for frontier AI models. Experts contribute subject matter knowledge across domains such as finance, healthcare, and engineering to improve how AI systems learn and perform.

Start Timeline & Availability

Roles are typically filled quickly, often within 48 hours. Candidates who can begin immediately are preferred.