Machine Learning Engineer (Production and Deployment)

As a Machine Learning Engineer (Production and Deployment) at Songfinch, you will play a crucial role in architecting, implementing, managing, and maintaining the machine learning production and deployment lifecycle. Your main responsibilities will include transitioning ML research prototypes into at-scale products, implementing efficient deployment strategies for ML models, and ensuring smooth functionality. Additionally, you will be responsible for integrating monitoring and troubleshooting tools, optimizing the ML prototype-to-product pipeline, and staying up-to-date with advancements in ML and AI. This is an exciting opportunity to directly influence the future of the company and the music industry as a whole.

• Implement efficient deployment strategies for ML models
• Rapidly transition and manage the continuous pipeline of ML research prototypes into at-scale products
• Productionize, package, and deploy ML applications
• Ensure smooth, logical, transparent functionality
• Integrate tools for monitoring, logging, alerting, troubleshooting, debugging deployed ML applications
• Continuously optimize our ML prototype-to-product pipeline
• Proactively identify potential bottlenecks and issues, and work with the broader team(s) to resolve them
• Partner with internal teams to troubleshoot and debug ML applications when needed
• Be in-tune with the developments and advancements of ML and AI
• Identify relevant trends, tools, tech, and processes that could be incorporated as potential differentiators (within production, deployment, and beyond)

• B.S. in Computer Engineering, Computer Science, Electrical Engineering, Physics, Applied Math, or other relevant STEM degrees
• 3+ years of hands-on experience with relevant tech/tools
• Basics (Conda, Git, GitHub, Python)
• DevOps Tools
• Containers (Docker, etc.)
• Container Orchestration (Kubernetes, Amazon Elastic Container Service, Amazon Elastic Kubernetes Service, etc.)
• Workflow Orchestration (Apache Airflow, Flyte, AWS Step Functions, AWS Lambda, etc.)
• AWS Cloud Computing Other (Elastic Compute Cloud, S3, Sagemaker, etc.)
• MLOps tools (MLflow, AWS Sagemaker, Kubeflow, etc.)
• ML platforms/frameworks/libraries (Keras, Matplotlib, NumPy, Pandas, PyTorch/Lightning, scikit-learn, TensorFlow, etc.)
• Adaptive, collaborative, and a problem-solving mentality
• Inherent builder of things, with an insatiable curiosity / desire to continuously learn
• A strong preference for (and

• Implement efficient deployment strategies for ML models
• Rapidly transition and manage the continuous pipeline of ML research prototypes into at-scale products
• Productionize, package, and deploy ML applications
• Ensure smooth, logical, transparent functionality
• Integrate tools for monitoring, logging, alerting, troubleshooting, debugging deployed ML applications
• Continuously optimize our ML prototype-to-product pipeline
• Proactively identify potential bottlenecks and issues, and work with the broader team(s) to resolve them
• Partner with internal teams to troubleshoot and debug ML applications when needed
• Be in-tune with the developments and advancements of ML and AI
• Identify relevant trends, tools, tech, and processes that could be incorporated as potential differentiators (within production, deployment, and beyond)
• Adaptive, collaborative, and a problem-solving mentality
• Inherent builder of things, with an insatiable curiosity / desire to continuously learn
• A strong preference for (and history of) hands-on and action-oriented execution
• Strong communication skills
• Self-motivated and ignited by fast-paced environments