We are looking for a talented and driven AI Solutions Engineer to join our team at the intersection of AI and music.
In this role, you'll operationalize cutting-edge ML models, ranging from in-house architectures to state-of-the-art open-source tools and LLMs, to build both music-driven applications and broader AI-powered experiences.
You will serve as a key bridge between ML research and backend engineering, deploying scalable, real-world systems that power intelligent tools across music and broader machine learning domains. Your responsibilities will span model adaptation, deployment, MLOps pipeline design, and performance optimization across cloud and edge environments.
Key Responsibilities
As an AI solutions engineer, you will work on a range of challenges. Some will be uniquely grounded in music and audio, while others will involve more general-purpose machine learning systems that support broader company initiatives.
Model Research & Adaptation
- Rapidly evaluate and adapt pretrained models, third-party APIs, and open-source frameworks
- Prototype, validate, and ship solutions tailored to product needs, including both music/audio-specific use cases and general ML applications
Deployment & Integration
- Package models into containerized services (Docker) and expose them via REST/gRPC APIs
- Integrate ML services with production backends in collaboration with software engineers
- Ensure reliability, fault-tolerance, and observability of deployed models in production
MLOps & Infrastructure
- Design and maintain end-to-end MLOps 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
- Investigate techniques for efficient on-device ML, especially for mobile and web
- Troubleshoot infrastructure and model-serving issues, ensuring consistent uptime and performance
Cross-functional Collaboration
- Work closely with ML researchers, software engineers, and product teams to align on goals and ensure smooth handoffs
- Collaborate closely with backend engineers to ensure seamless integration of ML systems within existing infrastructure
- Write clear, maintainable documentation for model APIs, deployment processes, and pipelines
Skills, Knowledge and Expertise
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- At least 2+ years of experience in ML Engineering, MLOps, or DevOps roles with a focus on machine learning
- Strong Python programming skills and experience with ML libraries such as PyTorch or TensorFlow
- Hands-on experience with Docker and building RESTful or gRPC APIs
- Familiarity with cloud services (AWS, GCP, or Azure) and orchestration platforms like Kubernetes
Preferred qualifications
- Experience with model hubs like Hugging Face or TensorFlow Hub
- Experience with model optimization, conversion, and export using ONNX and ONNX Runtime for deployment across diverse runtime environments
- Working knowledge of CI/CD tools (e.g., GitHub Actions, Jenkins, GitLab CI)
- Strong interest or background in music - composition, production, theory, or music/audio signal processing
