Senior/Staff Software Engineer, Frontier Research

The role

You will build systems that enable speech-to-text and text-to-speech foundation model experiments. This includes developing training infrastructure, data pipelines, and evaluation tooling as an individual contributor working with research scientists and product teams.

What you will do

• Architect horizontally scalable training systems to shorten model development cycles
• Build high-throughput pipelines for ingesting, versioning, and preprocessing unstructured audio and text
• Own research infrastructure including distributed training, job orchestration, experiment tracking, and storage
• Ship automated evaluation tooling for researchers
• Turn research proofs-of-concept into production-grade systems
• Build internal tools and dashboards for non-technical stakeholders
• Set technical direction for research infrastructure by writing design docs

Required qualifications

• Builds the first version of a system from a blank page with little direction
• 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
• Operates AI-native: uses and builds AI tools to amplify output
• Can quickly build useful internal UIs with AI tools
• Explains ML systems clearly to researchers and non-technical stakeholders

Nice to have

• 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

About the company

Deepgram is a research company that productizes as fast as possible. Their speech models and real-time infrastructure power AI systems for more than 5,000 organizations.