Remote C# Backend Engineer for AI Data Services

Alignerr Corp.; Seattle (WA); On-site; USD 12,398,000 - 17,909,000; Full time

Job summary

Alignerr is seeking a Senior C# Full-Stack Engineer to design and optimize data pipelines, annotation tooling, and evaluation systems powering AI labs. This fully remote contract role offers meaningful, high-impact work without legacy CRUD maintenance.

You'll build high-performance C# systems, develop backend services for large-scale data annotation and quality control, and collaborate closely with data, research, and engineering teams to support model training and evaluation infrastructure.

Qualifications

• 5+ years of professional experience writing production-grade C#.
• Experience with data modeling and high-performance SQL interactions.
• Experience with ORM frameworks or micro-ORMs.
• Ability to build secure, well-documented endpoints for data access.
• Commit to 20–40 hours per week.

Responsibilities

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

Skills

C# production-grade

Full-stack development

Systems programming

ORM / data modeling

API design

Job description

Alignerr is seeking a Senior C# Full-Stack Engineer to design and optimize data pipelines, annotation tooling, and evaluation systems powering AI labs. This fully remote contract role offers meaningful, high-impact work without legacy CRUD maintenance.

You'll build high-performance C# systems, develop backend services for large-scale data annotation and quality control, and collaborate closely with data, research, and engineering teams to support model training and evaluation infrastructure.