Machine Learning Researcher
ElevenLabsFull Time
Mid-level (3 to 4 years), Senior (5 to 8 years)
The candidate should have strong experience in training large-scale machine learning systems, particularly in STT or related speech domains. Proficiency with orchestration and infrastructure tools like Kubernetes, Docker, and Prefect, as well as familiarity with ML lifecycle tools such as MLflow, is required. Experience building internal tools or dashboards for non-technical users is also necessary.
The Machine Learning Engineer will architect and manage horizontally scalable training systems for STT and TTS models across diverse domains, including data preparation, training pipelines, and automated evaluation tooling. They will design and implement internal UIs and tools to make ML systems accessible to non-technical stakeholders, providing transparency and flexibility. Additionally, the role involves overseeing and managing training tooling, job orchestration, experiment tracking, and data storage.
Speech recognition APIs for audio transcription
Deepgram specializes in artificial intelligence for speech recognition, offering a set of APIs that developers can use to transcribe and understand audio content. Their technology allows clients, ranging from startups to large organizations like NASA, to process millions of audio minutes daily. Deepgram's APIs are designed to be fast, accurate, scalable, and cost-effective, making them suitable for businesses needing to handle large volumes of audio data. The company operates on a pay-per-use model, where clients are charged based on the amount of audio they transcribe, allowing Deepgram to grow its revenue alongside client usage. With a focus on the high-growth market of speech recognition, Deepgram is positioned for future success.