Data Infrastructure Engineer for Scalable AI Data Pipelines

Speechify

United States

On-site

USD 140,000 - 200,000

Full time

3 days ago
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Benefits offered by this job

Competitive salaries
Laid-back atmosphere
Asynchronous culture
Bonus
Equity

Job summary

Speechify is seeking a Software Engineer on the Data side of its AI team. You will help build high-quality datasets at petabyte scale and optimize ingestion pipelines to support model training.

Join a distributed, office-free company and collaborate with researchers to balance cost, throughput and quality while advancing Speechify’s next-generation products for consumers and enterprises.

Qualifications

  • BS/MS/PhD in Computer Science or related field.
  • 5+ years of industry experience in software development.
  • Proficiency with bash/Python scripting in Linux environments.
  • Proficiency in Docker and IaC concepts with at least one major Cloud Provider (GCP).
  • Experience with web crawlers and large-scale data processing is a plus.
  • Strong communication skills and ability to manage multiple tasks.

Responsibilities

  • Be scrappy to find new sources of audio data and bring it into the ingestion pipeline.
  • Operate and extend the cloud infrastructure for the ingestion pipeline (GCP, Terraform).
  • Collaborate with scientists to improve cost, throughput, and data quality for larger scale models.
  • Help craft the data team’s dataset roadmap to power Speechify’s consumer and enterprise products.

Skills

Bash scripting
Python scripting
Linux environments
GCP
Web crawlers

Education

BS/MS/PhD in Computer Science

Tools

Terraform
Docker
GCP

Job description

Speechify is seeking a Software Engineer on the Data side of its AI team. You will help build high-quality datasets at petabyte scale and optimize ingestion pipelines to support model training.

Join a distributed, office-free company and collaborate with researchers to balance cost, throughput and quality while advancing Speechify’s next-generation products for consumers and enterprises.

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