Remote AI Data Engineer: Build Scalable Training Pipelines

Speechify Inc.; Polska; Hybrid; PLN 117,000 - 428,000; Full time

Benefits offered by this job

Remote work

Stock options

Asynchronous culture

Job summary

Speechify Inc. is seeking a Software Engineer, AI Data to join its distributed AI team and build data pipelines powering model training and next-gen AI products. The role focuses on creating high-quality datasets at scale by combining engineering, infrastructure, and research.

You will explore new data sources, develop data-processing systems, and collaborate with AI scientists to improve training datasets, while taking ownership from discovery to production.

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.
• Strong problem‑solving skills, initiative, and the ability to work independently in ambiguous environments.

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

Backend development

Problem solving

Independent work

Linux basics

Education

Bachelor's degree in Computer Science or related field

Tools

Python

Bash

Terraform

GCP

Data pipelines

Job description

Speechify Inc. is seeking a Software Engineer, AI Data to join its distributed AI team and build data pipelines powering model training and next-gen AI products. The role focuses on creating high-quality datasets at scale by combining engineering, infrastructure, and research.

You will explore new data sources, develop data-processing systems, and collaborate with AI scientists to improve training datasets, while taking ownership from discovery to production.