Systems Software Engineer - Machine Learning Ops

Systems Software Engineer — Machine Learning Ops About The Role What if your systems engineering skills could directly shape the infrastructure powering the world's most advanced AI models? We're looking for a senior C++ engineer to build and optimize the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on to train and ship next-generation models. This is a fully remote, flexible contract role for a seasoned engineer who knows C++ deeply and thrives working on high-impact, production-grade systems. If you've spent years writing performance-critical code and want to apply that expertise at the cutting edge of AI development, this is the role for you. Organization: AlignerrType: Hourly ContractLocation: RemoteCommitment: 20–40 hours/week What You'll Do Design, build, and optimize high-performance C++ systems supporting AI data pipelines and evaluation workflowsDevelop full-stack tooling and backend services for large-scale data annotation, validation, and quality controlImprove reliability, performance, and safety across existing C++ codebases used in production AI environmentsCollaborate with data, research, and engineering teams to support model training and evaluation workflowsIdentify bottlenecks and edge cases in data and system behavior, and implement scalable, maintainable fixesParticipate in synchronous design reviews to iterate on system architecture and implementation decisions Who You Are Native or fluent English speaker with clear written and verbal communication skillsFull-stack developer with a strong systems programming background and deep C++ expertise5+ years of professional experience writing production C++ codeExperienced working with the C++ frontends of ML frameworks or inference runtimesFamiliar with hardware acceleration APIs for optimizing model inferenceAble to commit 20–40 hours per week with consistency and reliability Nice to Have Prior experience with data annotation, data quality pipelines, or evaluation systemsFamiliarity with AI/ML workflows, model training, or benchmarking pipelinesExperience with distributed systems design or developer toolingBackground in performance profiling, debugging, or systems reliability engineering Why Join Us Work on real production systems used by leading AI research labsFully remote and flexible — work when and where it suits youFreelance autonomy with the structure of meaningful, impactful technical workContribute directly to infrastructure that shapes the future of AI developmentPotential for ongoing work and contract extension as new projects launch