Senior Software Engineer - AI Data Pipelines (Remote)

Speechify; United States; Remote; USD 140,000 - 200,000; Full time

Benefits offered by this job

Autonomy

Asynchronous work culture

Competitive compensation

Equity

Impactful work

Job summary

Speechify is seeking a Software Engineer - AI Data to join our distributed AI team and build data pipelines powering model training and next-generation AI products. You will help create high-quality datasets at scale by combining engineering, infrastructure, and research.

The ideal candidate thrives on solving complex data problems, exploring new data sources, and building scalable systems that improve AI training efficiency while working in a fully remote, async environment.

Qualifications

• Bachelor's degree in Computer Science 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

Independent work

Problem-solving

Education

CS degree or equivalent

Tools

Python

Bash

Linux

GCP

Terraform

Job description

Speechify is seeking a Software Engineer - AI Data to join our distributed AI team and build data pipelines powering model training and next-generation AI products. You will help create high-quality datasets at scale by combining engineering, infrastructure, and research.

The ideal candidate thrives on solving complex data problems, exploring new data sources, and building scalable systems that improve AI training efficiency while working in a fully remote, async environment.