Meridial
SWE Infrastructure Specialist (Java) – Freelance AI Trainer Project
About this role
Freelance opportunity for experienced Java engineers to train AI models on infrastructure and systems design challenges. You'll evaluate how language models reason about enterprise Java architectures, distributed systems, and cloud deployments while providing detailed feedback to improve AI reasoning capabilities.
What you'll do
- Engage AI models on Java infrastructure and platform engineering scenarios
- Verify architectural decisions around concurrency, latency, throughput, and resource utilization
- Assess infrastructure testing strategies and deployment reliability risks
- Document failure modes and areas where models struggle with correctness and scalability
- Provide structured feedback to improve model evaluation frameworks
- Explain complex system behavior and design decisions in written form
What they're looking for
- Java and JVM-based systems
- Distributed systems architecture
- Cloud-native deployment patterns
- Performance engineering and optimization
- Systems thinking and design
- DevOps and SRE practices
- Technical writing and communication
- Quality assurance methodologies
Benefits
- $80–$150 per hour (rate varies by experience and location)
- 100% remote work globally
- Flexible freelance/contract arrangement
- Opportunity to influence AI model development
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
Meridial
Meridial trains and improves advanced AI models through expert evaluation and feedback across infrastructure, software engineering, machine learning, and language domains. The company hires experienced freelance specialists—including software engineers, ML experts, and language specialists—to test AI reasoning, identify failure modes, and provide detailed training data to enhance model capabilities.
View all jobs at MeridialLikely interview questions
- Describe a complex Java-based distributed system you've designed or optimized. What were the key performance bottlenecks, and how did you address concurrency, latency, and throughput trade-offs?
- Walk us through your experience with cloud-native deployments and infrastructure-as-code. Which platforms have you used (AWS, GCP, Azure), and how have you approached reliability and scalability?