Deepgram
Research Engineer, Machine Learning Systems
Remote Research role with clear candidate location fit.
PostedApr 23, 2026
Eligible countries1 accepted country
Seniority signalOpen level
Work settingRemote
Accepted candidate locations
USA
Role overview
Research Engineer, Machine Learning Systems
Requirements and responsibilities
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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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