Remote C# Data Pipeline Engineer for AI Training

Alignerr Corp.; United States; Remote; GBP 83,000 - 124,000; Part time

Job summary

Alignerr is seeking a Senior C# Infrastructure Engineer to design and build high-performance data pipelines, annotation tooling, and evaluation infrastructure for AI training. This remote, hourly contract role integrates with data, research, and engineering teams to impact real production systems.

You will work on scalable C# systems, streaming data pipelines, and backend tooling for large-scale data annotation and evaluation, contributing to reliability and performance across codebases.

Qualifications

• 5+ years of professional experience writing production-grade C#.
• Experience building streaming data pipelines and data annotation tooling.
• Strong English communication and remote collaboration.
• Ability to commit 20–40 hours per week.
• Experience with asynchronous streams and reactive patterns.

Responsibilities

• Design, build, and optimize high-performance C# systems powering AI data pipelines and evaluation workflows.
• Develop full-stack tooling and backend services for large-scale data annotation, validation, and quality control.
• Improve reliability, performance, and safety across existing C# codebases.
• Collaborate with data, research, and engineering teams to support model training and evaluation workflows.
• Identify bottlenecks and edge cases in data and system behavior, then implement scalable fixes.
• Participate in design reviews to iterate on architecture and implementation decisions.

Skills

C# development

Data pipelines

Asynchronous streams

Reactive programming

Distributed systems

Performance optimization

Job description

Alignerr is seeking a Senior C# Infrastructure Engineer to design and build high-performance data pipelines, annotation tooling, and evaluation infrastructure for AI training. This remote, hourly contract role integrates with data, research, and engineering teams to impact real production systems.

You will work on scalable C# systems, streaming data pipelines, and backend tooling for large-scale data annotation and evaluation, contributing to reliability and performance across codebases.