Senior AI Data Engineer — Build Scalable Data Pipelines

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

Benefits offered by this job

Competitive compensation

Asynchronous work environment

Autonomy in work

Impactful work

Job summary

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

You’ll collaborate with AI scientists to improve training datasets, own projects end-to-end, and contribute to data systems that scale cost-effectively in a distributed, remote team.

Qualifications

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

Responsibilities

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

Skills

Python

Bash

Linux

Data pipelines

Backend development

Production systems

Education

Bachelor's degree in Computer Science or equivalent

Tools

GCP

Terraform

Job description

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

You’ll collaborate with AI scientists to improve training datasets, own projects end-to-end, and contribute to data systems that scale cost-effectively in a distributed, remote team.