AI Data Engineer: Build Scalable Training Pipelines

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

Benefits offered by this job

Autonomy

Asynchronous work culture

Competitive compensation

Equity

Impactful product

Job summary

Speechify is seeking a Software Engineer - AI Data to join our AI team and build data pipelines powering model training and next-generation AI products. We create high-quality datasets at petabyte scale by combining engineering, infra, and research, solving complex data challenges for scalable AI.

The ideal candidate enjoys tackling hard data problems, exploring new data sources, and delivering production-ready data pipelines with a focus on quality and cost efficiency.

Qualifications

• Bachelor's degree in Computer Science or 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, especially data pipelines or large-scale data processing.

Responsibilities

• Discover new sources of audio data and build reliable pipelines for training datasets.
• Build and optimize large-scale data-processing systems for quality, throughput, and cost.
• Collaborate with AI Scientists to improve training datasets and model development.
• Take ownership of engineering projects from discovery to implementation and improvement.

Skills

5+ years software engineering

Python

Bash

Linux

Backend development

Problem-solving

Independent work

Education

CS degree or equivalent

Tools

Data pipelines

Large-scale data processing

Terraform

GCP

Job description

Speechify is seeking a Software Engineer - AI Data to join our AI team and build data pipelines powering model training and next-generation AI products. We create high-quality datasets at petabyte scale by combining engineering, infra, and research, solving complex data challenges for scalable AI.

The ideal candidate enjoys tackling hard data problems, exploring new data sources, and delivering production-ready data pipelines with a focus on quality and cost efficiency.