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 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 What You'll Do - 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 Who You Are - 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 Nice to Have - 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 Why Join Us - 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
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