We’re looking for an experienced and hands-on Senior Machine Learning Engineer with deep expertise in audio and speech technologies to drive the design, development, and deployment of advanced ML models powering our data integration platform. In this role, you will focus on speech-to-text (with domain adaptation), speaker identification, keyword spotting, language identification, and deep fake detection. You will work closely with engineering and data infrastructure teams to build, optimize, and deploy scalable ML services in production using NVIDIA Triton, Kubernetes, and modern transformer-based architectures.
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Responsibilities
- Assist ML team in developing, training, and deploying production-grade models for:
- Speech-to-text (including domain adaptation)
- Speaker identification and verification
- Keyword spotting
- Language identification
- Deepfake and synthetic audio detection
- Research and integrate transformer-based and multi-modal model architectures into production pipelines.
- Collaborate with platform engineers to deploy ML models via NVIDIA Triton Inference Server and ensure low-latency, scalable serving.
- Design and maintain Airflow DAGs for data preprocessing, feature extraction, and model enrichment pipelines.
- Ensure continuous improvement of models through retraining, performance monitoring, and data feedback loops.
- Collaborate cross-functionally with Data Engineering, Backend, and Product teams to integrate ML capabilities into the platform.
- Mentor and guide ML engineers and researchers, fostering a culture of technical rigor, creativity, and collaboration.
- Oversee release and validation processes for ML components within larger system releases.
- Contribute to and review production-level code in Python (C++, Golang, Rust knowledge is beneficial).
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Electrical Engineering, or related field
- Demonstrated record of leading ML initiatives from research to production.
- Experience in startups or fast-paced, product-centric teams is highly desirable.
