Systems Software Engineer - Machine Learning Ops
About this role
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: Alignerr - Type: Hourly Contract - Location: Remote - Commitment: 20–40 hours/week What You'll Do - Design, build, and optimize high-performance C++ systems supporting AI data pipelines and evaluation workflows - Develop full-stack tooling and backend services for large-scale data annotation, validation, and quality control - Improve reliability, performance, and safety across existing C++ codebases used in production AI environments - Collaborate with data, research, and engineering teams to support model training and evaluation workflows - Identify bottlenecks and edge cases in data and system behavior, and implement scalable, maintainable fixes - Participate 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 skills - Full-stack developer with a strong systems programming background and deep C++ expertise - 5+ years of professional experience writing production C++ code - Experienced working with the C++ frontends of ML frameworks or inference runtimes - Familiar with hardware acceleration APIs for optimizing model inference - Able to commit 20–40 hours per week with consistency and reliability Nice to Have - Prior experience with data annotation, data quality pipelines, or evaluation systems - Familiarity with AI/ML workflows, model training, or benchmarking pipelines - Experience with distributed systems design or developer tooling - Background in performance profiling, debugging, or systems reliability engineering Why Join Us - Work on real production systems used by leading AI research labs - Fully remote and flexible — work when and where it suits you - Freelance autonomy with the structure of meaningful, impactful technical work - Contribute directly to infrastructure that shapes the future of AI development - Potential for ongoing work and contract extension as new projects launch
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