Remote AI Data Engineer: Build Scalable ML Training Pipelines

Speechify

United States

Remote

USD 120,000 - 190,000

Full time

3 days ago
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Benefits offered by this job

Competitive compensation
Asynchronous work culture
Remote work

Job summary

Speechify is hiring a Software Engineer - AI Data to join our distributed AI team. You will build data pipelines powering model training and next-generation AI products, handling petabyte-scale datasets with cost-conscious design.

We value independent problem solving, strong Python/Bash skills, and experience with data pipelines or large-scale processing in Linux environments. This role is open to remote candidates globally.

Qualifications

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

Responsibilities

  • Discover new audio data sources and build reliable pipelines for training datasets.
  • Build and improve large-scale data-processing systems for quality, throughput, and cost.
  • Collaborate with AI scientists to enhance training datasets and models.
  • Own engineering projects from discovery through implementation and iteration.

Skills

Backend development
Python
Bash
Linux
Data pipelines
Problem solving

Education

Bachelor's or higher in Computer Science or related field

Tools

GCP
Terraform

Job description

Speechify is hiring a Software Engineer - AI Data to join our distributed AI team. You will build data pipelines powering model training and next-generation AI products, handling petabyte-scale datasets with cost-conscious design.

We value independent problem solving, strong Python/Bash skills, and experience with data pipelines or large-scale processing in Linux environments. This role is open to remote candidates globally.

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