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Salary: USD 140,000 - 200,000 / annual
Speechify is a text-to-speech platform used by over 50 million people to convert PDFs, books, Google Docs, news articles, and websites into audio. The company has ~200 employees globally in a fully distributed setting, with team members from Amazon, Microsoft, Google, Stanford, and other leading organizations.
You will join Speechify's AI team to build the data pipelines that power model training and next-generation AI products. The role focuses on discovering new audio data sources, building reliable pipelines to integrate them into training datasets, and optimizing large-scale data-processing systems for quality, throughput, and cost. You'll collaborate closely with AI Scientists to improve training datasets and support the development of next-generation models, taking ownership of engineering projects from problem discovery through implementation and continuous improvement.
Key responsibilities include:
- Discovering new sources of audio data and building reliable pipelines to bring them into training datasets
- Building and improving large-scale data-processing systems, optimizing for data quality, throughput, and cost
- Collaborating closely with AI Scientists to improve training datasets and support next-generation model development
- Taking ownership of engineering projects from problem discovery through implementation and continuous improvement
Requirements:
- Bachelor's degree in Computer Science or 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 ability to work independently in ambiguous environments
Nice to have:
- Experience with web crawlers, data acquisition, or distributed data-processing systems
- Experience with cloud infrastructure and Infrastructure as Code, particularly GCP and Terraform
- Familiarity with ML training data, dataset quality, or audio processing
