Software Engineer (C#) - Internal Tooling

About The Role

What if your C# expertise could directly shape the infrastructure powering the next generation of AI? We're looking for experienced full-stack C# engineers to build and optimize the data pipelines, annotation systems, and evaluation tooling that leading AI labs depend on every day.

This isn't maintenance work or low-stakes ticket-churning. You'll be working on real production systems at the frontier of AI development — the kind of infrastructure that determines how AI models are trained, measured, and improved.

• Organization: Alignerr
• Type: Hourly Contract
• Location: Remote
• Commitment: 20–40 hours/week

• Design, build, and optimize high-performance C# systems supporting large-scale AI data pipelines and evaluation workflows
• Develop full-stack tooling and backend services for data annotation, validation, and quality control at scale
• Improve the reliability, performance, and safety of existing C# codebases used in production AI environments
• Bridge the gap between .NET and Python ML ecosystems — invoking models, wrapping native libraries, and enabling smooth interoperability
• Build robust benchmarking harnesses to evaluate system performance and surface edge cases
• Collaborate with data, research, and engineering teams to support model training and evaluation workflows
• Participate in synchronous design reviews to iterate quickly on architecture and implementation decisions

• 3–5+ years of professional experience writing production-grade C#
• Experienced full-stack developer with a strong systems programming background
• Skilled at interoperability scenarios — calling Python ML models from .NET, wrapping native libraries, crossing runtime boundaries cleanly
• Experienced designing benchmarking and evaluation harnesses for real systems
• Clear, precise written and verbal communicator — you can explain a design decision as well as implement one
• Native or fluent English speaker
• Able to commit 20–40 hours per week reliably

• Prior experience with data annotation platforms, data quality systems, or evaluation pipelines
• Familiarity with AI/ML workflows, model training infrastructure, or benchmarking tooling
• Experience with distributed systems or internal developer tooling
• Background in performance engineering or systems-level optimization

• Work on cutting-edge AI infrastructure alongside leading research labs and engineering teams
• Fully remote and flexible — structure your hours around your life
• Freelance autonomy with the depth and substance of meaningful, long-term engineering work
• Make a direct, tangible impact on how AI systems are built, evaluated, and improved at scale
• Potential for ongoing work and expanded scope as new projects launch