Senior Remote C++ HPC Engineer — AI Infrastructure

Alignerr Corp.; Seattle (WA); On-site; USD 124,000 - 207,000; Full time

Job summary

Alignerr is seeking a Senior C++ Engineer for HPC work in AI, focusing on high-performance data pipelines, annotation tooling, and evaluation infrastructure. This fully remote, hourly contract role expects 20–40 hours per week from a seasoned systems engineer.

You will design and optimize C++ systems, develop tooling for annotation and quality control, and collaborate with data and research teams to accelerate model training and evaluation cycles.

Qualifications

• 5+ years of production-grade C++ experience.
• Strong understanding of computer architecture, SIMD intrinsics, and cache coherency.
• Experience with lock-free programming and maximizing parallelism using threading libraries.

Responsibilities

• Design, build, and optimize high-performance C++ systems for 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, throughput, and safety across production C++ codebases.
• Identify performance bottlenecks and implement scalable fixes.
• Collaborate with data, research, and engineering teams to accelerate model training and evaluation cycles.
• Participate in design reviews to iterate on system architecture and implementation decisions.

Skills

Production-grade C++

C++17/C++20

System programming

Parallelism/Threading

English communication

20–40 hours/week

SIMD intrinsics

Lock-free programming

Tools

SIMD intrinsics

Job description

Alignerr is seeking a Senior C++ Engineer for HPC work in AI, focusing on high-performance data pipelines, annotation tooling, and evaluation infrastructure. This fully remote, hourly contract role expects 20–40 hours per week from a seasoned systems engineer.

You will design and optimize C++ systems, develop tooling for annotation and quality control, and collaborate with data and research teams to accelerate model training and evaluation cycles.