Remote Python Engineer for AI Data Pipelines & Workflows

Alignerr Corp.; New York (NY); A distancia; USD 83.000 - 165.000; A tiempo parcial

Ventajas ofrecidas por este puesto de trabajo

Remote work

Flexible hours

Descripción de la vacante

Alignerr is seeking a senior Python Software Engineer to design and optimize data pipelines, annotation tooling, and evaluation systems for AI workflows. This fully remote hourly contract offers 20–40 hours per week and the chance to impact how AI models are trained and improved.

You will build production-grade Python systems, scale tooling, and collaborate with cross-functional teams across AI research and engineering to deliver robust infrastructure for data annotation and evaluation.

Formación

• 3–5+ years of professional experience writing production-grade Python.
• Disciplined about code quality — apply linters, formatters, and test coverage.
• Experience integrating AI services and third-party APIs with strong error handling.

Responsabilidades

• Design, build, and optimize high-performance Python systems for AI data pipelines.
• Develop full-stack tooling and backend services for large-scale data annotation and validation.
• Identify bottlenecks, edge cases, and reliability issues across existing codebases.
• Improve performance, safety, and maintainability of Python infrastructure used in production environments.
• Collaborate with data, research, and engineering teams to support model training/evaluation workflows.
• Participate in design reviews and iterate on system architecture and implementation decisions.

Conocimientos

Python

Full-stack

Systems programming

Testing & quality

API integration

Descripción del empleo

Alignerr is seeking a senior Python Software Engineer to design and optimize data pipelines, annotation tooling, and evaluation systems for AI workflows. This fully remote hourly contract offers 20–40 hours per week and the chance to impact how AI models are trained and improved.

You will build production-grade Python systems, scale tooling, and collaborate with cross-functional teams across AI research and engineering to deliver robust infrastructure for data annotation and evaluation.