Remote C++ Systems Engineer for AI Data Pipelines

Alignerr Corp.; New York (NY); Remote; USD 83,000 - 131,000; Part time

Benefits offered by this job

Fully remote

Flexible schedule

Contract extension potential

Job summary

Alignerr is seeking a seasoned C++ engineer to design and optimize high-performance data pipelines, annotation tooling, and evaluation systems for AI labs. This fully remote hourly contract offers challenging work and collaboration with research and engineering teams focused on correctness, performance, and scale.

You will build end-to-end systems, implement fast serialization, and drive reliability across large-scale AI workflows while committing 20-40 hours per week.

Qualifications

• 3-5+ years writing production-grade C++
• Strong systems programming background with full-stack development
• Expert-level knowledge of efficient binary serialization formats
• Hands-on experience implementing zero-copy data exchange protocols
• Native or fluent English with clear written and verbal communication

Responsibilities

• Design, build, and optimize high-performance C++ systems for AI data pipelines
• Develop tooling and backend services for data annotation and validation
• Implement fast binary serialization formats and zero-copy data exchange
• Identify and fix performance bottlenecks and reliability issues in complex codebases
• Collaborate with data, research, and engineering teams on model training/workflows
• Participate in design reviews to refine architecture and implementation

Skills

C++

Systems programming

Full-stack development

Zero-copy data exchange

Binary serialization

Job description

Alignerr is seeking a seasoned C++ engineer to design and optimize high-performance data pipelines, annotation tooling, and evaluation systems for AI labs. This fully remote hourly contract offers challenging work and collaboration with research and engineering teams focused on correctness, performance, and scale.

You will build end-to-end systems, implement fast serialization, and drive reliability across large-scale AI workflows while committing 20-40 hours per week.