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Salary: USD 30,000 - 75,000 / 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 distributed globally with no physical office, drawing talent from Amazon, Microsoft, Google, Stanford, and high-growth startups like Stripe and Vercel.
You will join Speechify's AI team to build the data pipelines that power model training and next-generation AI products. The focus is on constructing 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 end-to-end, from problem discovery through implementation and continuous improvement
The role offers autonomy in a dynamic, distributed environment where your contributions directly shape the company and its products. You'll work at the intersection of artificial intelligence and audio in a rapidly evolving domain, with the opportunity to impact a product that changes lives for people 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
