Research Engineer, Machine Learning Systems
Deepgram · USA
About this role
**Company Overview** Deepgram is the leading platform underpinning the emerging Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. With voice-native foundation models available via cloud APIs or self-hosted/on-prem software, Deepgram delivers high accuracy, low latency, and cost efficiency—having processed over 50,000 years of audio and transcribed more than 1 trillion words. **Operating Rhythm (AI-first)** Deepgram expects an AI-first mindset: actively use and experiment with advanced AI tools, and even build your own into everyday work. Success depends on consistent, creative use of the latest AI capabilities, rapid adoption of new models/modes, and continuous iteration. **The Opportunity** Voice is the most natural modality for human-machine interaction, but current sequence modeling paradigms can’t yet deliver universal human interaction. The core challenges stem from fundamental audio data issues: real-world audio is scarce and highly diverse (voices, speaking styles, acoustic conditions), and the high dimensionality makes training and deployment computationally and storage intensive at world scale. **Role: Research Engineer, Machine Learning Systems** Deepgram is seeking a highly skilled and versatile Machine Learning Engineer to join the Research team. You’ll partner with research scientists to prototype and validate novel modeling ideas, then scale them through robust training systems for speech technologies, internal tooling, and innovative data strategies. **Key Responsibilities** - **Scalable Model Training:** Architect and manage horizontally scalable systems to accelerate the end-to-end training lifecycle for STT and TTS. Focus on faster, more efficient model development via optimized data preparation/management, high-throughput training pipelines, distributed infrastructure, and automated evaluation tooling. - **Tooling & Accessibility:** Design and implement internal UIs and tools that make ML systems and workflows accessible to non-technical stakeholders, with transparency and flexibility. - **Infrastructure & Tools:** Oversee training tooling, job orchestration, experiment tracking, and data storage. **The Challenge / What We’re Looking For** - See “unsolved” problems as opportunities to pioneer new approaches. - Identify the critical experiment that validates (or kills) an idea in days, not months. - Scale successful proofs-of-concept by **100x**. - Use AI to automate and amplify your own impact. - Strong drive toward elegant, scalable solutions. **Required / Important Skills** - Strong experience with the machine learning research pipeline, especially in STT or related speech domains (experimenting with/evaluating architectures and implementing large-scale training systems). - Proficiency with orchestration and infrastructure tools such as **Kubernetes, Docker, and Prefect**. - Familiarity with ML lifecycle tools such as **MLflow**. - Experience building internal tools or dashboards for non-technical users. - Hands-on data engineering experience for unstructured audio and text data. - Comfortable working cross-functionally with researchers, engineers, and product stakeholders. **Why Join Deepgram?** Help shape the future of human-machine communication. The research culture emphasizes ownership, experimentation, and real-world impact—empowering you to build tools and systems that accelerate ML research and product deploy
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