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About this role
Role overview Join the data side of an AI engineering team as a Software Engineer focused on building the datasets that power next-generation text-to-speech models. The role spans the full lifecycle of data acquisition, from sourcing new audio material to operating the cloud infrastructure that ingests it at petabyte scale. You will partner closely with research scientists and team leadership to push the cost, throughput, and quality frontier for training data.
Responsibilities - Scout and onboard new sources of audio data, integrating them into the existing ingestion pipeline. - Operate and extend cloud infrastructure for the ingestion stack, currently running on GCP and managed with Terraform. - Work hand-in-hand with scientists to balance cost, throughput, and quality as datasets grow. - Contribute to the dataset roadmap alongside AI teammates and engineering leadership. - Support downstream consumer and enterprise products by delivering richer data at larger scale and lower cost.
Requirements - BS, MS, or PhD in Computer Science or a related field. - 5+ years of industry software development experience. - Proficiency in bash and Python scripting within Linux environments. - Strong grasp of Docker and Infrastructure-as-Code concepts, with hands-on experience on at least one major cloud provider (GCP used internally). - Ability to juggle multiple tasks and adapt as priorities shift. - Clear written and verbal communication skills.
Nice to have - Experience building web crawlers and designing large-scale data processing workflows.
Benefits and work setup - 100% distributed, async-friendly work environment with no physical office. - Entrepreneurial team culture that encourages initiative, ownership, and fast iteration. - Hands-off management style aimed at giving engineers space to do their best work. - Competitive base salary of $140,000–$200,000 plus bonus and equity, depending on experience. - Mission-driven product that supports users with dyslexia, ADHD, low vision, concussions, autism, and other learning differences.