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Builds and scales cloud infrastructure to collect and process petabyte-scale audio data for AI model training, using GCP, Terraform, and Python on Speechify’s text-to-speech platform.
Build and scale Speechify’s data ingestion pipeline to collect petabyte-scale audio datasets for AI model training, using GCP, Terraform, and Python on Linux.
Builds and maintains petabyte-scale data pipelines for AI model training at a text-to-speech startup, using GCP, Terraform, and Python to ingest and process audio data.
Builds and scales cloud infrastructure to collect and process petabyte-scale audio datasets for AI model training at a fast-growing text-to-speech startup.
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.
Builds and maintains petabyte-scale data pipelines for AI model training, including web scraping, cloud infrastructure, and GCP-based ingestion systems.
Builds and maintains petabyte-scale data pipelines for AI model training at Speechify, including web crawlers, cloud infrastructure on GCP, and ingestion workflows.
Builds and maintains petabyte-scale data pipelines for AI model training at a fast-growing text-to-speech company, using GCP, Terraform, and Python.
Builds and maintains petabyte-scale data pipelines for AI model training at a fast-growing text-to-speech startup, using GCP, Terraform, and Python.
Builds and maintains petabyte-scale data pipelines for AI model training, sourcing audio data and operating cloud infrastructure on GCP with Terraform.
Builds and maintains petabyte-scale data pipelines for AI model training, sourcing audio/text data and operating cloud infrastructure on GCP with Terraform.
Builds and maintains Speechify’s petabyte-scale data ingestion pipeline on GCP, sourcing audio/text data to train AI models powering the company’s text-to-speech products.
Builds and scales Speechify’s petabyte-scale data pipelines for AI model training, integrating web crawlers, cloud infrastructure, and GCP services to collect and process audio/text data.
The Computational Electromagnetics Software Developer will design, implement, and optimize parallel scalable software for CEM applications. The role involves benchmarking algorithms, deploying code on HPC systems, and supporting integration into the company's digital transformation roadmap.
Builds and maintains Speechify’s petabyte-scale data ingestion pipeline on GCP, sourcing audio data and collaborating with AI scientists to improve model training datasets.
Builds and scales cloud infrastructure to collect and process petabyte-scale audio data for AI model training, using GCP, Terraform, and Python on Linux.
Builds and maintains petabyte-scale data pipelines on GCP to collect and process audio/text datasets for AI model training, collaborating with research scientists to improve cost, throughput, and quality.
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