Lead Machine Learning Engineer, Python, GoLang, AWS

• Participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms
• Focus on machine learning architectural design
• Develop and review model and application code
• Ensure high availability and performance of machine learning applications
• Design, build, and/or deliver ML models and components that solve real-world business problems in collaboration with Product and Data Science teams
• Inform ML infrastructure decisions using understanding of modeling techniques and issues, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Collaborate with a cross-functional Agile team to create and enhance software enabling big data and ML applications
• Retrain, maintain, and monitor models in production
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines to feed ML models
• Apply continuous integration and continuous deployment practices, including test automation and monitoring
• Ensure code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and ML follows Responsible and Explainable AI best practices
• Continuously learn and apply the latest innovations and best practices in machine learning engineering

Requirements

• Bachelor’s Degree
• At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
• At least 4 years of experience programming with Python, Scala, or Java
• At least 2 years of experience building, scaling, and optimizing ML systems
• Preferred: Master’s or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
• Preferred: 3+ years of experience building production-ready data pipelines that feed ML models
• No agencies please

Core Competencies

Demonstrates expertise in machine learning architectural design, model development, and optimization, with a strong focus on building scalable and efficient ML systems. Proficient in leveraging cloud-based technologies and implementing best practices in responsible AI and continuous integration.

Highest-signal resume keywords

• Machine Learning Architectural Design
• Python Programming
• Building Production-Ready Data Pipelines
• Cloud-Based Architectures
• Continuous Integration and Deployment

Hard Skills

• Machine Learning Applications
• Model Development
• Data Pipeline Construction
• Hyperparameter Tuning
• Model Validation
• Distributed Computing
• Application Code Development
• Automated Testing
• Performance Optimization
• Feature Selection

Soft Skills

• Collaboration
• Problem Solving
• Continuous Learning
• Cross-Functional Teamwork
• Communication

Industry Keywords

• Responsible AI
• Explainable AI
• Data-Intensive Solutions
• Machine Learning Engineering
• Model Governance

Tools & Technologies

• Cloud Technologies
• Agile Methodologies
• ML Frameworks
• Data Science Tools
• Big Data Technologies