Principal Python Engineer - ML Infrastructure

Production engineering

Build and improve Python infrastructure used for AI data processing and model evaluation. This role combines systems programming, distributed computing, backend services, and full-stack tooling in production environments.

• Develop fast, scalable Python services for annotation, validation, and quality control.
• Diagnose compute-intensive bottlenecks using profiling and advanced asynchronous techniques.
• Strengthen the safety, reliability, and performance of existing code.
• Work with data, engineering, and research colleagues to improve training and evaluation workflows.
• Guide architecture through live design discussions and clear technical explanations.

Required background

At least five years of production Python experience in large infrastructure or comparable engineering environments is required. You need deep knowledge of distributed computing, concurrency, and advanced async patterns, together with a full-stack and systems programming background.

Understanding Python internals is essential, including GIL constraints, memory profiling, and optimization for compute-heavy workloads. You must communicate technical strategy confidently, speak English fluently or natively, and be available for 20 to 40 hours per week.

Additional experience

Annotation tools, data quality systems, and evaluation infrastructure are useful backgrounds. Familiarity with AI or ML workflows, model training, benchmark pipelines, distributed architecture, or developer tools is also welcome, as is direct work with AI labs.

Contract arrangement

This is a fully remote hourly contract with flexible working arrangements and a 20 to 40 hour weekly commitment. The work centers on substantive production systems alongside engineers and researchers, with possible continued assignments or contract extension as projects begin.