Research Engineer, Machine Learning Systems
Builds scalable ML training systems and infrastructure for speech AI models (STT/TTS), prototypes novel ideas with researchers, and creates internal tools for cross-functional teams. Requires strong ML research pipeline experience, especially in speech domains, plus orchestration tools expertise.
About the job
Key Responsibilities
- Scalable Model Training: Architect and manage horizontally scalable systems to accelerate end-to-end training lifecycle for STT and TTS models, including data preparation, high-throughput pipelines, distributed infrastructure, and automated evaluation tooling.
- Tooling & Accessibility: Design and implement internal UIs and tools making ML systems accessible to non-technical stakeholders.
- Infrastructure & Tools: Oversee training tooling, job orchestration, experiment tracking, and data storage.
It's Important to Us That You Have
- Strong experience with the machine learning research pipeline, particularly in STT or related speech domains, experimenting with architectures, 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.
Skills
Machine Learning, Speech-To-Text, Stt, Text-To-Speech, Tts, Kubernetes, Docker, Prefect, MLflow, Data Engineering, Distributed Training, Ml Pipelines
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