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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 other leading organizations.
You'll join Speechify's AI team to build the data pipelines and infrastructure that power model training and next-generation AI products. The focus is on creating high-quality datasets at petabyte scale while optimizing for cost and efficiency through a combination of engineering, infrastructure, and research.
Key responsibilities:
- Discover and integrate new audio data sources into training datasets
- Design and optimize large-scale data-processing systems for quality, throughput, and cost efficiency
- Partner 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
You'll work in a dynamic, autonomous environment that values innovation and impact. The role sits at the intersection of AI and audio technology, a rapidly evolving domain. Speechify's products have been recognized by Google (Chrome Extension of the Year) and Apple (2025 Design Award for Inclusivity), and the company is particularly focused on removing barriers to learning for people with dyslexia, ADD, and other learning differences.
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
- Production systems experience, 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
- Cloud infrastructure and Infrastructure as Code experience (GCP, Terraform preferred)
- Familiarity with ML training data, dataset quality, or audio processing
