Synthetic Data Pipeline Engineer — Remote

Pragmatike; Stati Uniti; Ibrido; USD 120.000 - 190.000; Tempo pieno

Descrizione del lavoro

Pragmatike is seeking a hands-on research engineer to build a synthetic data pipeline that turns professional workflows into useful AI training tasks. This role spans task design, generation systems, evaluation and analysis of what models learn, taking early research through to dependable pipelines.

You will collaborate with domain experts to create varied, realistic tasks, design generation approaches, and build tools to mutate and validate data, aiming for reliable operation across time zones.

Competenze

• Proficiency with Python, Docker, and Linux.
• Experience with synthetic data research methods and an understanding of the quality and limitations of synthetic data.
• Experience building synthetic data pipelines end to end, including work without a prescribed roadmap.
• Clear written and spoken English and ability to collaborate across time zones.

Mansioni

• Work with subject‑matter experts to develop training tasks across professional and technical domains.
• Design generation approaches that produce varied, realistic, and learnable tasks.
• Build tools to mutate, validate, and improve generated tasks.
• Evaluate model and agent results to identify what tasks teach and where performance breaks down.
• Create measures for task diversity, realism, learnability, and related qualities.
• Develop pipeline methods and systems from initial research into reliable operation.

Conoscenze

Synthetic data research methods

End-to-end data pipelines

Collaboration across time zones

English communication

Strumenti

Python

Docker

Linux

Descrizione del lavoro

Pragmatike is seeking a hands-on research engineer to build a synthetic data pipeline that turns professional workflows into useful AI training tasks. This role spans task design, generation systems, evaluation and analysis of what models learn, taking early research through to dependable pipelines.

You will collaborate with domain experts to create varied, realistic tasks, design generation approaches, and build tools to mutate and validate data, aiming for reliable operation across time zones.