Remote ML Infrastructure Engineer (C++)

Alignerr Corp.; Seattle (WA); Remote; USD 83,000 - 165,000; Part time

Job summary

Alignerr is seeking a Systems Software Engineer — Machine Learning Ops to design and optimize high-performance C++ systems powering large-scale AI data pipelines and evaluation workflows. This remote, hourly contract role asks for deep systems programming experience and collaboration with researchers and engineers.

You will build full-stack tooling for data annotation, validation, and quality control, improve reliability across production ML code, and participate in architecture reviews to shape

Qualifications

• 5+ years writing production-grade C++
• Hands-on with ML framework frontends or inference runtimes
• Experience with hardware acceleration APIs for performance

Responsibilities

• Design, build, and optimize high-performance C++ systems supporting 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 used in production ML environments
• Collaborate with research, data, and engineering teams to support model training and benchmarking workflows
• Identify bottlenecks, edge cases, and failure modes in data and system behavior — and implement scalable, production-ready fixes
• Participate in synchronous design reviews to iterate on architecture and implementation decisions

Skills

C++

Systems programming

ML frameworks

Hardware acceleration

English fluency

Job description

Alignerr is seeking a Systems Software Engineer — Machine Learning Ops to design and optimize high-performance C++ systems powering large-scale AI data pipelines and evaluation workflows. This remote, hourly contract role asks for deep systems programming experience and collaboration with researchers and engineers.

You will build full-stack tooling for data annotation, validation, and quality control, improve reliability across production ML code, and participate in architecture reviews to shape