Remote AI Data Engineer for Scalable Training Pipelines

Speechify Inc.; Oregon; Hybride; USD 140 000 - 200 000; Plein temps

Avantages offerts par ce poste

Competitive compensation

Asynchronous work culture

Welcoming atmosphere

Autonomy and impact

Résumé du poste

Speechify Inc. is seeking a Software Engineer - AI Data to join its distributed AI team and build data pipelines powering model training and next-generation AI products.

You will create high-quality datasets at scale, merging engineering, infrastructure, and research to reduce cost and improve quality. You will discover new data sources, optimize data-processing systems, and collaborate with AI scientists to advance training data.

Qualifications

• Degree in Computer Science or a 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, ideally involving data pipelines or large-scale data processing.
• Strong problem-solving skills, initiative, and the ability to work independently in ambiguous environments.

Responsabilités

• 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.

Connaissances

Python

Bash

Linux

Backend development

Data pipelines

Formation

BS in Computer Science or equivalent

Outils

GCP

Terraform

Description du poste

Speechify Inc. is seeking a Software Engineer - AI Data to join its distributed AI team and build data pipelines powering model training and next-generation AI products.

You will create high-quality datasets at scale, merging engineering, infrastructure, and research to reduce cost and improve quality. You will discover new data sources, optimize data-processing systems, and collaborate with AI scientists to advance training data.