Software Engineer (C#) - Internal Tooling

Software Engineer (C#) — Internal Tooling (AI Infrastructure) About The Role What if your C# expertise could directly shape the infrastructure powering the next generation of AI? We're looking for a senior full-stack C# engineer to build the data pipelines, annotation systems, and evaluation tooling that leading AI labs depend on every day. This isn't toy work or proof-of-concept territory. You'll be writing production code that sits at the heart of real AI training and evaluation workflows — the kind of systems that determine how models learn, improve, and get measured. Organization: Alignerr Type: Hourly Contract Location: Remote Commitment: 20–40 hours/week What You'll Do Design and build high-performance C# systems that support 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 reliability, performance, and correctness across existing C# codebases Build robust benchmarking and evaluation harnesses to measure system behavior Implement interoperability solutions — such as invoking Python ML models from .NET or wrapping native libraries Identify bottlenecks and edge cases, then ship scalable, well-reasoned fixes Collaborate with data, research, and engineering teams across model training and evaluation workflows Participate in synchronous design reviews to iterate on architecture and implementation decisions Who You Are 3–5+ years of professional experience writing production-grade C# Strong full-stack developer with a solid systems programming foundation Experienced in interoperability scenarios — calling Python ML models from .NET, wrapping native libraries, bridging ecosystems Proven track record designing benchmarking harnesses and performance evaluation systems Clear, precise written and verbal communicator — you can explain technical decisions to mixed audiences Native or fluent English speaker Able to commit 20–40 hours per week consistently Nice to Have Prior experience with data annotation platforms, data quality pipelines, or evaluation systems Familiarity with AI/ML workflows, model training, or benchmarking infrastructure Experience with distributed systems or developer tooling Background working in fast-moving research or AI-adjacent engineering environments Why Join Us Work on real production systems alongside top AI research labs — not toy demos Fully remote and flexible — structure your hours around your best work Freelance autonomy with the substance of meaningful, high-stakes engineering Make a tangible impact on the infrastructure that shapes how next-generation AI models are built and evaluated Potential for ongoing work and contract extension as projects grow