Deepgram
Research Engineer, Machine Learning Systems
Rol remoto de Research con fit claro de ubicación del candidato.
Publicado23 abr 2026
Países elegibles1 país aceptado
Señal de seniorityNivel abierto
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Estados Unidos
Resumen del rol
Research Engineer, Machine Learning Systems
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
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. 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.
The Challenge
- See "unsolved" problems as opportunities to pioneer entirely new approaches
- Can identify the one critical experiment that will validate or kill an idea in days, not months
- Have the vision to scale successful proofs-of-concept 100x
- Are obsessed with using AI to automate and amplify your own impact
It's Important to Us That You Have
- 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.
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