AI Music Technologist

3 days ago

Singapore beBeeArtificial Full time $1,200,000 - $1,500,000

Immerse yourself in the convergence of music and AI, where innovation knows no bounds

Key Responsibilities

A visionary technologist will drive cutting-edge advancements at the intersection of artificial intelligence and music.

This role entails operationalizing pioneering machine learning models for both music-driven applications and broader AI-powered experiences. As a bridge between research and engineering, you will deploy scalable systems that power intelligent tools across music and machine learning domains.

Core Competencies:
  • Evaluate and adapt pretrained models, third-party APIs, and open-source frameworks to align with product requirements.
  • Develop tailored solutions, leveraging music/audio-specific use cases and general machine learning applications to drive business outcomes.
Technical Expertise
  • Containerize models into services using Docker, exposing them via REST/gRPC APIs.
  • Integrate machine learning services with production backends, collaborating closely with software engineers.
  • Ensure reliability, fault-tolerance, and observability of deployed models in production environments.
Machine Learning Operations & Infrastructure
  • Design and maintain end-to-end machine learning pipelines for training, versioning, deployment, and monitoring.
  • Manage deployments on cloud platforms (AWS, GCP, Azure) using orchestration tools like Kubernetes.
Performance Optimization
  • Monitor and optimize model inference for low latency and high throughput performance.
  • Investigate techniques for efficient on-device machine learning, particularly for mobile and web applications.
  • Troubleshoot infrastructure and model-serving issues, ensuring consistent uptime and performance.
Cross-functional Collaboration
  • Collaborate closely with researchers, software engineers, and product teams to ensure seamless handoffs and goal alignment.
  • Work collaboratively with backend engineers to integrate ML systems within existing infrastructure.
  • Write clear, maintainable documentation for model APIs, deployment processes, and pipelines.

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