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Salary: USD 30,000 - 55,000 / 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 the data pipelines that power model training and next-generation AI products. The role focuses on discovering and integrating new audio data sources, building large-scale data-processing systems optimized for quality, throughput, and cost, and collaborating with AI Scientists to improve training datasets. You'll take ownership of engineering projects from discovery through implementation and continuous improvement.
Key responsibilities include:
- Discovering new sources of audio data and building reliable pipelines for 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 iteration
The company emphasizes a dynamic, innovation-focused culture with autonomy, asynchronous work practices, and the opportunity to impact a product that serves 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
