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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 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 role focuses on discovering new audio data sources, building reliable ingestion pipelines, 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 ingest them into training datasets
- Building and improving large-scale data-processing systems, optimizing for data quality, throughput, and cost
- Collaborating 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
