Remote Backend Engineer Jobs (NOW HIRING)

Role Title: Senior Backend Engineer

Role Type: Contractor (20 hrs perweek)

Location: Remote

micro1 is engaging Senior Backend Engineers to participate in an advanced project for a customer, focused on creating sophisticated Reinforcement Learning Environments for AI model training and evaluation. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required - your domain knowledge is what matters.

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. You will develop realistic scenarios involving distributed systems, networking, IAM, queues, durable storage, observability, rolling deployments, and disaster recovery, then build reproducible environments, deterministic validation tests, golden reference solutions, and intentionally defective variants.

Scope of Work

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

Preferred Qualifications

1. Proven expertise with backend programming languages, such as C++, Python, Rust, GoLang, JAVA, or JavaScript.
2. Strong practical experience with DevOps, cloud infrastructure, CI/CD pipelines, and automation tools.
3. Demonstrated ability to architect, scale, and secure distributed systems in production-grade environments.
4. Deep understanding of networking, IAM, queues, durable storage, and disaster recovery concepts.

Process:

1. Apply to the role, filling out the screening questions
2. Complete AI interview (aprox. 30 minutes), reviewed by recruiters)
3. Hiring Manager review

Compensation Structure

Compensation is output-based; experts are paid per task that meets the project specifications. 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.

micro1 is a US-based technology company focused on AI-powered hiring and talent solutions. It connects companies with highly skilled remote technical professionals and uses AI-driven assessments and interviews to evaluate candidates.

The company works across areas such as software engineering, AI/ML, data, cloud, DevOps, and other technical domains. For specialized projects, micro1 also engages experts to contribute to AI training and evaluation, including creating real-world technical environments and scenarios that help improve AI models.

For this particular opportunity, candidates are being engaged as remote contractors for approximately 20 hours per week, working on advanced AI projects where their backend/cloud expertise is used to create realistic environments for training and evaluating AI systems.