Remote AI Data Engineer - Build Scalable Training Pipelines

Speechify; Województwo pomorskie; Hybrid; PLN 117,000 - 428,000; Full time

Benefits offered by this job

Remote work

Stock options

Competitive compensation

Job summary

Speechify, a fully distributed company, seeks a Software Engineer - AI Data to join its AI team and build data pipelines powering model training and next-generation AI products. We create petabyte-scale datasets by merging engineering, infrastructure, and research.

You will discover new data sources, optimize data-processing systems for quality and cost, and collaborate with AI scientists to improve datasets and models. Ownership from discovery to delivery is encouraged.

Qualifications

• Degree in Computer Science or related field, or equivalent practical experience.
• 5+ years of professional software engineering experience with backend focus.
• Proficiency in Python and Bash in Linux environments.
• Experience building and operating production data pipelines and large-scale data processing.
• Strong problem-solving skills and ability to work independently in ambiguous environments.

Responsibilities

• Discover new sources of audio data and build pipelines to bring them into 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 development of models.
• Take ownership of engineering projects from problem discovery through implementation and continuous improvement.

Skills

Python

Bash

Backend development

Data pipelines

Problem solving

Education

BSc in Computer Science

Tools

Linux

Cloud (GCP)

Terraform

Job description

Speechify, a fully distributed company, seeks a Software Engineer - AI Data to join its AI team and build data pipelines powering model training and next-generation AI products. We create petabyte-scale datasets by merging engineering, infrastructure, and research.

You will discover new data sources, optimize data-processing systems for quality and cost, and collaborate with AI scientists to improve datasets and models. Ownership from discovery to delivery is encouraged.