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Build and maintain Speechify's native Windows desktop app using WinUI, C#, and XAML, ensuring accessibility, performance, and cross-version compatibility for millions of users.
Build and scale Speechify’s data ingestion pipeline to collect petabyte-scale audio datasets for AI model training, using GCP, Terraform, and Python on a fully distributed team.
Build and scale Speechify’s data ingestion pipeline to collect and process petabyte-scale audio datasets for AI model training, using GCP, Terraform, and Python.
Build and scale Speechify’s petabyte-scale data pipeline for AI model training, integrating cloud infrastructure, web crawlers, and data ingestion on GCP.
This role involves developing and maintaining automated test frameworks and test plans to ensure the quality of Apple's Identity Management Services across iOS, macOS, and Web platforms. The engineer will collaborate with cross-functional teams to automate testing processes and improve software delivery efficiency.
Build and scale Speechify’s data pipelines to collect and process petabyte-scale audio/text datasets for AI model training, using GCP, Terraform, and Python.
Build and scale Speechify’s data ingestion pipeline to collect petabyte-scale audio datasets for AI model training, using GCP, Terraform, and Python.
Build and scale Speechify’s data ingestion pipeline to collect petabyte-scale audio datasets for AI model training, using GCP, Terraform, and Python on a fully remote team.
Software Engineer responsible for data collection and infrastructure to support AI model training at Speechify, a text-to-speech company.
Builds and scales Speechify’s petabyte-scale data pipelines for AI model training, integrating web crawlers, cloud infrastructure on GCP, and Python tooling to acquire and process audio/text datasets.
Builds and scales Speechify’s petabyte-scale data pipelines for AI model training, integrating web crawlers, cloud infrastructure, and GCP/Terraform to ingest audio and text sources.
Builds and scales Speechify’s petabyte-scale audio data pipeline on GCP, sourcing and ingesting text-to-speech training data while collaborating with AI researchers to improve model performance.
Builds and scales Speechify’s petabyte-scale data pipelines for AI model training, integrating web crawlers, cloud infrastructure on GCP, and Python tooling to acquire and process audio/text data.
Build and scale data pipelines to collect and process petabyte-scale audio datasets for AI model training, using GCP, Terraform, and Python on a fully remote team.
Builds and scales cloud infrastructure to collect and process petabyte-scale audio data for training AI text-to-speech models, using GCP, Terraform, and Python.
Build and scale Speechify’s data ingestion pipeline to collect and process audio/text datasets for AI model training, using GCP, Terraform, and Python.
Builds and scales data pipelines to collect and process petabyte-scale audio datasets for AI model training, using GCP, Terraform, and Python on Speechify’s text-to-speech platform.
Builds and scales data pipelines to collect and process petabyte-scale audio datasets for AI model training, using GCP, Terraform, and Python on a distributed team.
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