Research Engineer, Machine Learning Systems - Deepgram

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’. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software.

The Role

Deepgram is seeking a highly skilled and versatile Machine Learning Engineer to join our Research team. As a Member of the Research Staff, you will 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 that dramatically accelerate the end-to-end training lifecycle for Speech-to-Text (STT) and Text-to-Speech (TTS) models through optimized data preparation, 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 across the company.
• Infrastructure & Tools: Oversee and manage training tooling, job orchestration, experiment tracking, and data storage.

Qualifications

• Strong experience with the machine learning research pipeline, particularly in STT or related speech domains.
• Proficiency with orchestration and infrastructure tools like Kubernetes, Docker, and Prefect.
• Familiarity with ML lifecycle tools such as MLflow.
• Experience building internal tools or dashboards for non-technical users.
• Hands-on experience with data engineering practices for unstructured audio and text data.
• Comfortable working in cross-functional teams that include researchers, engineers, and product stakeholders.