Senior/Staff Software Engineer, Frontier Research

Company Operating Rhythm

Team Overview

Deepgram Research builds the speech-to-text and text-to-speech foundation models behind our platform. Today's scaling approaches won't deliver universal voice AI on their own. Audio is scarce, diverse, and high-dimensional, so we need new approaches. As the Research team's software engineer, you'll build the systems that make those experiments possible: training infrastructure, data pipelines, and evaluation tooling. You'll be an individual contributor working side by side with research scientists, data and infrastructure engineers, and product. Most of this work starts from a blank page, so you'll spend less time waiting on tickets and more time writing the spec.

Key Goals

• Shorten the STT/TTS model development cycle, from data prep 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 validate or kill ideas quickly
• Turn promising research proofs-of-concept into production-grade systems that ship in Deepgram's STT and TTS models
• Build internal tools and dashboards that give non-technical stakeholders visibility into, and control over, ML workflows
• Set technical direction for research infrastructure by writing the design docs that define what gets built next

Minimum Skills, Knowledge & Capabilities

• Builds the first version of a system from a blank page with little direction, and writes the design or spec others build from
• Hands-on with large-scale distributed training, including multi-node GPU training and high-throughput data pipelines
• Fluent with container orchestration and ML lifecycle tooling such as Kubernetes, Docker, Prefect, and MLflow, or their equivalents
• Engineers data systems for large volumes of unstructured data, covering storage, versioning, and preprocessing at scale
• Has taken a research prototype from notebook to a system other people rely on
• Partners closely with researchers through shifting timelines and failed experiments, and can find the one experiment that settles a question
• Operates AI-native: uses and builds AI tools to amplify your own output, and can quickly build useful internal UIs with them
• Explains ML systems clearly to researchers and non-technical stakeholders alike

Preferred Qualifications ( Nice-to-have, not required)

• Hands-on training or evaluating speech/audio models (ASR, STT, TTS), or LLM experience with a clear path to audio
• Has worked embedded with a research team where priorities shift with experimental results
• Contributions to open-source speech or ML tooling (e.g., ESPnet, NeMo, SpeechBrain, WhisperX)
• Typically ~5–12 years building software or ML systems, or equivalent demonstrated experience; advanced degree or equivalent industry experience.

We know the confidence gap and imposter syndrome can get in the way of meeting great candidates — so please don't hesitate to apply. We'd love to hear from you.