Senior/Staff Software Engineer, Frontier Research

Deepgram; San Francisco, Ann Arbor (CA, MI); Hybride; USD 180 000 - 260 000; Plein temps

Résumé du poste

Deepgram is seeking an expert Software/ML Infrastructure Engineer to build and own the end-to-end research infrastructure. You will design scalable training systems, implement high-throughput data pipelines, and collaborate with researchers to move experiments into production-grade components.

You will also develop internal tools and dashboards, shape the technical direction, and ensure robust evaluation tooling to accelerate discovery within our STT/TTS platforms.

Qualifications

• 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

Responsabilités

• Shorten the STT/TTS model development cycle 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
• Ship automated evaluation tooling so researchers can validate or kill ideas quickly
• Turn promising research proofs-of-concept into production-grade systems
• Build internal tools and dashboards that give non-technical stakeholders visibility into ML workflows
• Set technical direction for research infrastructure by writing design docs

Connaissances

First version system development

Distributed training

Kubernetes

Docker

Prefect

MLflow

Data pipelines

Communication with researchers

Outils

Kubernetes

Docker

Prefect

MLflow

Description du poste

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 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.

Notice: We're aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @deepgram.com email address. If you've received a message claiming to be Deepgram, please forward it to [e-mail].