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Deepgram

Builds voice AI APIs for developers

Senior/Staff Software Engineer - Frontier Research

AI ResearchUpdated on Nov 13
Full-timeRemote in USA$197k - $338k /yrRemote

About the Job

Requirements

  • Build the first version of a system from a blank page with little direction, and write the design or specifications others build from.
  • Have hands-on experience with large-scale distributed training, including multi-node GPU training and high-throughput data pipelines.
  • Be fluent with container orchestration and machine-learning lifecycle tooling such as Kubernetes, Docker, Prefect, and MLflow, or equivalent tools.
  • Engineer data systems for large volumes of unstructured data, including storage, versioning, and preprocessing at scale.
  • Have taken a research prototype from notebook to a system other people rely on.
  • Partner closely with researchers through shifting timelines and failed experiments, and identify the experiment that settles a question.
  • Operate AI-natively by using and building AI tools to amplify output, and quickly build useful internal user interfaces with them.
  • Explain machine-learning systems clearly to researchers and non-technical stakeholders alike.

Responsibilities

  • Shorten the speech-to-text and text-to-speech model development cycle, from data preparation through training and evaluation, by architecting horizontally scalable training systems.
  • Build high-throughput pipelines that ingest, version, and preprocess large, diverse sets of unstructured audio and text.
  • Own research infrastructure end to end, including distributed training, job orchestration, experiment tracking, and storage, in partnership with the Infrastructure team.
  • Ship automated evaluation tooling so researchers can quickly validate or discontinue ideas.
  • Turn promising research proofs of concept into production-grade systems used in Deepgram's speech-to-text and text-to-speech models.
  • Build internal tools and dashboards that give non-technical stakeholders visibility into and control over machine-learning workflows.
  • Set technical direction for research infrastructure by writing design documents that define what gets built next.

Desired Qualifications

  • Hands-on experience training or evaluating speech/audio models (automatic speech recognition, speech-to-text, or text-to-speech), or large language model experience with a clear path to audio.
  • Experience working embedded with a research team where priorities shift with experimental results.
  • Contributions to open-source speech or machine-learning tooling such as ESPnet, NeMo, SpeechBrain, or WhisperX.
  • Typically around 5–12 years building software or machine-learning systems, or equivalent demonstrated experience; an advanced degree or equivalent industry experience.

About the Company

Deepgram

Builds voice AI APIs for developers

Deepgram builds speech software for developers and businesses that turn spoken conversations into text, generate voices, and run automated phone agents. Its Nova recognition models process live audio and recordings, with tools to identify speakers and recognize industry terms. Developers send audio or text through its APIs and receive transcripts, spoken responses, or extracted information that their applications use in customer service and other workflows. The company sells usage priced by audio minutes or generated characters, with prepaid credits and enterprise contracts for larger deployments. Deepgram was founded in 2015 and is headquartered in San Francisco, California, United States.

Company size
51-200
Company stage
Series C
Total funding
$233.3M
Headquarters
San Francisco, CA
Founded
2015

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