Research Engineer, Machine Learning Systems

Deepgram

15h

About The Position

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. You'll work at the intersection of machine learning, data infrastructure, and internal tooling to support our mission of building world-class speech recognition and synthesis systems. On the Research team, you will experiment with new technologies and techniques, while also working on product-focused deliverables, learning from colleagues with a wide range of expertise in AI and machine learning as you go.

Requirements

Strong experience with the machine learning research pipeline, particularly in STT or related speech domains. This includes experimenting with and evaluating new architectures and modeling approaches, and implementing large-scale training systems.
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.

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. This includes far more than automated training: the role focuses on making model development significantly faster and more efficient through optimized data preparation and 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 across the company. These UIs should be designed to provide transparency and flexibility to internally built tooling.
Infrastructure & Tools: Oversee and manage training tooling, job orchestration, experiment tracking, and data storage.