Research Engineer, Machine Learning Systems

Stack

Responsibilities

• ▹Architect and manage horizontally scalable systems that accelerate STT/TTS model training end-to-end
• ▹Optimize data preparation and management, high-throughput training pipelines, and automated evaluation tooling
• ▹Design and implement internal UIs and tools that make ML workflows accessible to non-technical stakeholders
• ▹Oversee training tooling, job orchestration, experiment tracking, and data storage

Requirements

• ▹Strong experience with the ML research pipeline, particularly STT or related speech domains
• ▹Experience evaluating new architectures/modeling approaches and implementing large-scale training systems
• ▹Proficiency with orchestration/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 data engineering experience with unstructured audio and text data

Nice to have

• ▹Comfortable working in cross-functional teams with researchers, engineers, and product stakeholders

Soft skills

About the company

Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT) and text-to-speech (TTS), and powering production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings on Deepgram's technology, including Twilio, Cloudflare, and Sierra. Backed by a recent Series C, Deepgram has processed over 50,000 years of audio and transcribed more than a trillion words.