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Salary: USD 30,000 - 110,000 / 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 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 efficiency.
Key responsibilities:
- Discover new sources of audio data and construct reliable pipelines to incorporate them into training datasets
- Build and improve large-scale data-processing systems, optimizing for data quality, throughput, and cost
- Collaborate closely with AI Scientists to enhance training datasets and support development of next-generation models
- Own engineering projects from problem discovery through implementation and continuous improvement
This is a strong fit for engineers who enjoy solving complex problems, exploring novel data sources, and building systems that make AI training more efficient and scalable. You'll work in an asynchronous, distributed environment with autonomy and focus on a product that impacts users with learning differences like dyslexia and ADD.
**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
