Lead Machine Learning Engineer, Python, GoLang, AWS

Jobtailor; California (MO); On-site; USD 140,000 - 200,000; Full time

Job summary

Jobtailor is seeking a Senior Machine Learning Engineer to design and implement scalable ML applications in a distributed environment. You will build production-grade data pipelines, advance ML model deployment, and collaborate with Product and Data Science teams.

The role emphasizes cloud-based architectures, responsible AI practices, and continuous integration/monitoring to ensure high availability and performance of ML systems.

Qualifications

• Bachelor’s Degree required.
• 6+ years designing and building data-intensive solutions using distributed computing.
• 4+ years programming with Python, Scala, or Java.
• 2+ years building, scaling, and optimizing ML systems.
• Master’s or Doctoral Degree in CS/EE/Math preferred.
• 3+ years building production-ready data pipelines for ML models preferred.
• No agencies please.

Responsibilities

• Participate in the detailed technical design, development, and implementation of ML applications.
• Focus on ML architectural design.
• Develop and review model and application code.
• Ensure high availability and performance of ML applications.
• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure decisions including model choice, data/feature selection, training, and validation.
• Solve complex problems by writing and testing code, developing/validating ML models, and automating tests and deployment.
• Collaborate with cross-functional Agile team to enable big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage/build cloud-based architectures to deliver optimized ML models at scale.
• Construct optimized data pipelines feeding ML models.
• Apply CI/CD practices including test automation and monitoring.
• Ensure code governance and responsible/Explainable AI practices.

Skills

Machine Learning Architectural Design

Python Programming

Building Production-Ready Data Pipeli​

Cloud-Based Architectures

Continuous Integration and Deployment

Distributed Computing

Model Development

Hyperparameter Tuning

Model Validation

Agile Methodologies

Big Data Technologies

ML Frameworks

Data Science Tools

Education

Bachelor’s Degree

Master’s or Doctoral Degree in CS/EE/Math (preferred)

Tools

Cloud Platforms

ML Frameworks

Data Science Tools

Big Data Technologies

Agile Methodologies

Job description

• 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