Systems Software Engineer - Machine Learning Ops

Systems Software Engineer — Machine Learning Ops (AI Infrastructure)About The RoleWhat if your expertise in systems programming could directly shape the infrastructure powering the next generation of AI? We're looking for seasoned C++ engineers to build high-performance data pipelines, annotation tooling, and evaluation systems used by leading AI research labs.This isn't theoretical work. You'll be writing production code that sits at the heart of real model training and evaluation workflows — alongside engineers and researchers pushing the boundaries of what AI can do.Organization: AlignerrType: Hourly ContractLocation: RemoteCommitment: 20–40 hours/weekWhat You'll DoDesign, build, and optimize high-performance C++ systems supporting large-scale AI data pipelines and evaluation workflowsDevelop full-stack tooling and backend services for data annotation, validation, and quality control at scaleImprove reliability, performance, and correctness across existing C++ codebases used in production ML environmentsCollaborate with research, data, and engineering teams to support model training and benchmarking workflowsIdentify bottlenecks, edge cases, and failure modes in data and system behavior — and implement scalable, production-ready fixesParticipate in synchronous design reviews to iterate on architecture and implementation decisionsWho You AreNative or fluent English speaker with clear written and verbal communication skillsFull-stack developer with a strong systems programming foundation5+ years of professional experience writing production-grade C++Hands-on experience with C++ frontends of ML frameworks or inference runtimesFamiliar with hardware acceleration APIs for optimizing model inference performanceAble to commit 20–40 hours per week with consistent availabilityNice to HavePrior experience with data annotation pipelines, data quality systems, or evaluation infrastructureFamiliarity with AI/ML workflows, model training loops, or benchmarking frameworksExperience building distributed systems or developer-facing toolingBackground working alongside ML researchers or in a fast-moving research engineering environmentWhy Join UsWork directly on cutting-edge AI systems alongside top research labs — this is frontier-level infrastructure workFully remote and async-friendly — work from wherever you do your best thinkingFreelance flexibility with the depth and impact of a high-stakes engineering roleSee your work translate directly into improvements in how next-generation AI models are built and evaluatedPotential for extended engagement and expanded scope as projects evolve