Remote AI Data Systems Engineer — Build Training Pipelines

Speechify Inc.; Polska; Hybrid; PLN 544,000 - 777,000; Full time

Benefits offered by this job

Competitive compensation

Equity

Asynchronous work culture

Remote distribution

Job summary

Speechify Inc. is seeking a Software Engineer - AI Data to join its AI team and build data pipelines that power model training and next-generation AI products.

The role emphasizes crafting high-quality datasets at scale and collaborating with researchers to improve training efficiency. The candidate will explore new data sources, optimize data quality and cost, and own projects from discovery to deployment in a fully distributed, asynchronous work culture.

Qualifications

• A degree in Computer Science or a related field, or equivalent practical experience.
• 5+ years of professional software engineering experience, with strong backend development fundamentals.
• Proficiency in Python and Bash in Linux environments.
• Experience building and operating production systems, ideally involving data pipelines or large-scale data processing.

Responsibilities

• Discover new sources of audio data and build reliable pipelines to bring them into our training datasets.
• Build and improve large-scale data-processing systems, optimizing for data quality, throughput, and cost.
• Collaborate closely with AI Scientists to improve training datasets and support the development of next-generation models.
• Take ownership of engineering projects from problem discovery through implementation and continuous improvement.

Skills

Python

Bash

Backend development

Data pipelines

Education

Bachelor's degree in CS or related field

Tools

GCP

Terraform

Job description

Speechify Inc. is seeking a Software Engineer - AI Data to join its AI team and build data pipelines that power model training and next-generation AI products.

The role emphasizes crafting high-quality datasets at scale and collaborating with researchers to improve training efficiency. The candidate will explore new data sources, optimize data quality and cost, and own projects from discovery to deployment in a fully distributed, asynchronous work culture.