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 responsibility will be to transition and manage ML research prototypes into at-scale products, ensuring smooth functionality and efficient deployment strategies. Additionally, you will optimize the ML prototype-to-product pipeline, identify potential bottlenecks, and stay up-to-date with the latest developments in ML and AI. With the opportunity to directly influence the future of the company and the music industry, this role offers a unique chance to make a significant impact.

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

• Adaptive, collaborative, and problem-solving work environment
• Opportunity for continuous learning and development
• Hands-on and action-oriented execution
• Strong communication skills
• Fast-paced work environment
• Equal opportunity employer with a commitment to inclusivity and diversity