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 adaptable — 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