Data Infrastructure Engineer (Rust) - High Performance Computing

Data Infrastructure Engineer (Rust) — AI Data Pipelines & High Performance Computing About the Role What if your mastery of Rust could directly shape the infrastructure behind the world's most advanced AI systems? We're looking for a Senior Rust Full-Stack Engineer to design and build the high-performance data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on — at production scale, on real research workflows that matter. This is a fully remote contract role for engineers who care deeply about performance, correctness, and systems that don't just work — they fly . Organization : Alignerr Type : Hourly Contract Location : Remote Commitment : 20–40 hours/week What You'll Do Design, build, and optimize high-performance systems in Rust supporting AI data pipelines and evaluation workflows at scale Develop full-stack tooling and backend services for large-scale data annotation , validation, and quality control Hunt down bottlenecks — memory layout issues, missed SIMD opportunities, unnecessary heap allocations — and eliminate them Improve reliability, performance, and safety across existing production Rust codebases Collaborate closely with data, research, and engineering teams to support model training and evaluation workflows Participate in synchronous design reviews to iterate on architecture and implementation decisions Who You Are Native or fluent English speaker with clear written and verbal communication skills 5+ years of professional experience writing production-grade Rust Deep expertise in data parallelism and concurrency patterns — you know how to saturate a CPU Strong command of memory layout, SIMD optimizations, and heap allocation minimization — performance bottlenecks don't hide from you Self-directed and reliable, able to commit 20–40 hours per week to meaningful technical work Nice to Have Prior experience with data annotation , data quality pipelines, or evaluation systems Familiarity with AI/ML workflows, model traini