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 versátil - 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
Possible for ongoing work and contract extension as projects grow