Software Engineer (C#) - Internal Tooling
About this role
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
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