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Salary: USD 30,000 - 80,000 / 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. With ~200 employees distributed globally and no physical offices, Speechify is building next-generation AI products focused on making reading accessible to all learners.
You will join the AI team as a Software Engineer focused on AI Data, responsible for building and maintaining the data pipelines that power model training and next-generation AI products. The role involves discovering new audio data sources, building reliable ingestion pipelines, and optimizing large-scale data-processing systems for quality, throughput, and cost efficiency.
Key responsibilities include:
- Discovering and integrating new audio data sources into training datasets
- Building and improving large-scale data-processing systems with focus on data quality, throughput, and cost optimization
- Collaborating with AI Scientists to enhance training datasets and support next-generation model development
- Owning engineering projects end-to-end from problem discovery through implementation and continuous improvement
The role is fully remote and based in a 100% distributed company culture emphasizing autonomy, asynchronous work, and independent problem-solving in ambiguous environments.
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
NICE TO HAVE:
- Experience with web crawlers, data acquisition, or distributed data-processing systems
- Experience with cloud infrastructure and Infrastructure as Code (GCP, Terraform)
- Familiarity with ML training data, dataset quality, or audio processing
