# Senior/Staff Software Engineer, Frontier Research at Deepgram

Deepgram is hiring a Senior/Staff Software Engineer, Frontier Research for a remote role open to applicants in United States. It pays $150k-$250k a year and Level rates it Builds AI ●●●●; you can [apply on Deepgram](https://jobsbylevel.com/go/1dd2010b-fae1-417c-be8b-420aeb5cb44a?utm_medium=ai-agent).

AI Level 4, AI centrality 87 out of 100. Remote (USA - Remote).

## Details

- Company: [Deepgram](https://jobsbylevel.com/companies/deepgram)
- AI level: AI Level 4 (score 87 out of 100)
- Location: Remote (USA - Remote)
- Salary: $150k-$250k
- Posted: August 23, 2026
- Apply: https://jobsbylevel.com/go/1dd2010b-fae1-417c-be8b-420aeb5cb44a?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-staff-software-engineer-frontier-research-at-deepgram-e7fb71

## Prepare for this job

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## Description

Company Overview Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram. Company Operating Rhythm At Deepgram, we expect an AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance. Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do. Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5. 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

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