Senior C++ Systems Engineer, ML Ops (Remote)

Alignerr Corp.; New York (NY); Remote; USD 96,000 - 152,000; Part time

Job summary

Alignerr is seeking a senior Systems Software Engineer (Machine Learning Ops) to design and optimize production-grade C++ systems powering AI data pipelines and evaluation workflows. This fully remote, hourly contract role requires deep C++ expertise and a track record of building scalable, reliable software for large-scale AI workloads.

You will develop tooling and backend services for data annotation and validation, collaborating across data, research, and engineering teams.

Qualifications

• 5+ years of professional experience writing production C++ code.
• Strong background in systems programming and performance-focused software.
• Experience with ML frameworks frontends or inference runtimes is a plus.

Responsibilities

• 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.

Skills

C++

Production code

Full-stack development

ML frameworks frontend

Hardware acceleration APIs

Job description

Alignerr is seeking a senior Systems Software Engineer (Machine Learning Ops) to design and optimize production-grade C++ systems powering AI data pipelines and evaluation workflows. This fully remote, hourly contract role requires deep C++ expertise and a track record of building scalable, reliable software for large-scale AI workloads.

You will develop tooling and backend services for data annotation and validation, collaborating across data, research, and engineering teams.