SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
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 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 data pipelines that power model training and next-generation AI products. The role focuses on discovering and acquiring high-quality audio datasets at petabyte scale while optimizing for cost and efficiency. You'll work at the intersection of data engineering, infrastructure, and AI research.
Key responsibilities:
- Discover new sources of audio data and build reliable pipelines to ingest 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 improve training datasets and support next-generation model development
- Take ownership of engineering projects from problem discovery through implementation and continuous improvement
The company emphasizes autonomy, asynchronous work culture, and the opportunity to work on products that impact 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
