Engineering Manager, Machine Learning Job Description Template

The Machine Learning Engineering Manager is responsible for leading the data science, machine learning, and similar activities across our business. This role works closely with other engineers and machine learning specialists to find innovative ways to apply emerging technologies to improve business processes and drive more value for customers. The ideal candidate will have a strong background in machine learning model development, natural language processing and understanding, and data analysis which they can utilize to manage and improve our company’s AI/ML initiatives. 

Typical Duties and Responsibilities

• Build and manage a world-class team of data engineerings, MLOps engineers, and machine learning engineers
• Guide the team to architect an entire AI Engineering platform that allows for deployment and scalability of machine learning models
• Build tools and capabilities that help with data ingestion to feature engineering, data management and organization
• Build tools and capabilities for distributed optimization
• Work with stakeholders on the research and software engineering side of the company to understand how to support their teams
• Build tools and capabilities for model management and model performance monitoring
• Guide the team to operating services at internet scale with high availability and reliability
• Propose and implement the best engineering and research practices for scaling ML-powered features, with a goal to enable the fast iteration of and efficient experimentation with novel features
• Contribute to and influence the ML infrastructure roadmap based on the feedback from internal customers

Education

• Master’s degree or higher in machine learning, artificial intelligence, software engineering or a related field

Required Skills and Experience

• 5+ years of experience in machine learning
• 2+ years of experience in a leadership role
• Experience and understanding of the entire machine learning pipeline from data ingestion to production
• Experience with machine learning operations, software engineering, and architecture
• Experience with large-scale systems including parallel computing
• Experience architecting and building an AI platform that supports productionization of ML models
• Experience with MLOps systems
• Strong programming skills in a scientific computing language such as Python 
• Experience using frameworks for machine learning and data science like scikit-learn, pandas, NumPy
• Experience working with ML tools such as Tensorflow, Keras, and Pytorch
• Ability to take successful, complex research ideas from experimentation to production
• Excellent written and oral communication skills and the capability to drive cross-functional requirements with product and engineering teams