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Machine Learning Engineer
2 months ago
We are a pioneering company in Generative Voice AI, revolutionizing content creation and consumption across various industries. Our cutting-edge text-to-speech generator, coupled with real-time APIs, enables the creation of immersive voice-centric experiences. Our technology is trusted by creatives and corporations alike for applications in advertising, gaming, virtual assistance, and call centers, delivering flexibility and realism in speech like never before.
About the RoleAs a Machine Learning Engineer focusing on inference and serving models in production, you will be instrumental in operationalizing and optimizing our AI models for live environments. This role places you at the heart of our production team, where you will ensure the seamless deployment and scalability of our voice AI technologies.
Key Responsibilities- Model Deployment and Maintenance: Implement, monitor, and maintain machine learning models in production environments to ensure high availability and low latency.
- Collaboration and Optimization: Collaborate with data scientists and developers to optimize model performance, including resource management and cost efficiency.
- System Design and Development: Design and develop robust systems for real-time inference and batch processing of voice data.
- Model Lifecycle Management: Manage the lifecycle of models from deployment to updates and scaling, ensuring smooth transitions and minimal downtime.
- Automation and Tooling: Develop tools and frameworks to automate model training, testing, validation, and deployment processes.
- Technology Evaluation and Integration: Continuously evaluate and incorporate state-of-the-art ML deployment practices and technologies into the infrastructure.
- Product Integration: Work closely with product teams to integrate AI capabilities into customer-facing applications.
- Production Experience: Strong experience in deploying and managing machine learning models in production.
- Deployment Tools and Cloud Platforms: Proficient with ML deployment tools (e.g., TensorFlow Serving, TorchServe) and cloud platforms (AWS, GCP, Azure).
- Programming Skills: Expertise in Python and familiarity with software development best practices.
- Containerization and Orchestration: Knowledge of containerization and orchestration technologies (Docker, Kubernetes).
- Problem-Solving and Analytical Skills: Excellent problem-solving, troubleshooting, and analytical skills.
- Education and Experience: Degree in computer science, engineering, or related field with a specialization in machine learning.
- Voice AI Experience: Experience with voice or speech recognition technologies.
- Performance Tuning and Optimization: Familiarity with performance tuning and optimization of real-time systems.
- Open-Source Contributions: Contributions to open-source projects or active engagement in the ML community.
