Research Engineer, Machine Learning Systems
$150k - $250k • Remote • USA - Remote • FullTime
Posted 1mo ago
Job Location
USA - Remote
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Machine Learning Engineer
About the job
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.
Responsibilities
- Architect and manage horizontally scalable systems to accelerate the end-to-end training lifecycle for Speech-to-Text (STT) and Text-to-Speech (TTS) models.
- Optimize model development through efficient data preparation, high-throughput training pipelines, distributed infrastructure, and automated evaluation tooling.
- Design and implement internal UIs and tools to make ML systems and workflows accessible to non-technical stakeholders.
- Oversee and manage training tooling, job orchestration, experiment tracking, and data storage.
Requirements
- Strong experience with the machine learning research pipeline, particularly in STT or related speech domains.
- Experience experimenting with and evaluating new architectures and modeling approaches.
- Experience 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.
- Comfort working in cross-functional teams including researchers, engineers, and product stakeholders.