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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 was named Chrome Extension of the Year by Google and received Apple's 2025 Design Award for Inclusivity. Speechify operates as a 100% distributed company with ~200 employees globally, including engineers from Amazon, Microsoft, Google, and leading PhD programs.
You will join Speechify's AI team to build data pipelines that power model training and next-generation AI products. The team focuses on creating high-quality datasets at petabyte scale while optimizing for cost, combining engineering, infrastructure, and research expertise.
Key responsibilities:
- Discover new sources of audio data and build reliable pipelines to integrate 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 next-generation model development
- Own engineering projects from problem discovery through implementation and continuous improvement
The role emphasizes solving complex problems, exploring novel data sources, and building systems that make AI training more efficient and scalable. You will 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
