Lead Machine Learning Engineer

Jobtailor; Town of Texas (WI); On-site; USD 120,000 - 180,000; Full time

Job summary

Jobtailor is seeking a senior ML Architect to join an Agile team dedicated to productionizing machine learning applications at scale. You will design, develop, and implement ML solutions using Python, Scala, or Java across AWS, Azure, and Google Cloud platforms.

The role focuses on ML architectural design, model optimization, data pipelines, and the end-to-end delivery of production models. You will collaborate with Product and Data Science teams, apply CI/CD practices, monitor models, and

Qualifications

• Bachelor’s degree in CS, EE, math, or related field.
• 6+ years designing data-intensive distributed solutions.
• 4+ years Python/Scala/Java programming.
• 2+ years building ML systems at scale.
• No immigration sponsorship for employment authorization.
• Preferred: Master’s or Doctoral in CS/EE/Math.
• Preferred: 3+ years building production-ready data pipelines.
• Preferred: 3+ years with ML frameworks like scikit-learn, PyTorch, Spark, TensorFlow.
• Preferred: 2+ years of leadership experience.
• Preferred: 2+ years with AWS/Azure/GCP.

Responsibilities

• Participate in an Agile team for productionizing ML applications at scale.
• Design, develop, and implement ML applications.
• Focus on ML architectural design.
• Develop and review model and application code.
• Ensure high availability and performance of ML apps.
• Design, build, deliver ML models and components solving business problems with Product and Data Science teams.
• Inform ML infrastructure decisions on data, training, validation, etc.
• Write and test code, develop and validate ML models, and automate tests and deployment.
• Collaborate with cross-functional Agile team to enable big data and ML apps.
• Retrain, maintain, and monitor production models.
• Leverage/build cloud-based architectures to deliver optimized ML models at scale.
• Construct optimized data pipelines feeding ML models.
• Apply CI/CD with test automation and monitoring.
• Manage code to reduce vulnerabilities and govern models with Responsible and Explainable AI.
• Use Python, Scala, or Java.

Skills

Python Programming

Scala

Java

Distributed Computing

Data Pipeline Construction

Leadership

Education

Bachelor’s Degree

Master’s or Doctoral Degree

Tools

AWS

Azure

Google Cloud Platform

Scikit-learn

PyTorch

Dask

Spark

TensorFlow

Job description

• Participate in an Agile team dedicated to productionizing machine learning applications and systems at scale
• Design, develop, and implement 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 deliver ML models and components solving real-world business problems in collaboration with Product and Data Science teams
• Inform ML infrastructure decisions using modeling techniques and considerations such as model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
• Write and test application code, develop and validate ML models, and automate 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 production models
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines feeding ML models
• Apply continuous integration and continuous deployment practices, including test automation and monitoring
• Manage code to reduce vulnerabilities, govern models from a risk perspective, and apply Responsible and Explainable AI best practices
• Use Python, Scala, or Java

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
• No employer sponsorship or immigration-related support for employment authorization
• 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
• Preferred: 3+ years of on-the-job experience with an industry-recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
• Preferred: 2+ years of experience developing performant, resilient, and maintainable code
• Preferred: 2+ years of experience with data gathering and preparation for ML models
• Preferred: 2+ years of people leader experience
• Preferred: 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation
• Preferred: Experience developing and deploying ML solutions in AWS, Azure, or Google Cloud Platform
• Preferred: Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
• Preferred: ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
• Preferred: Experience leveraging interactive AI tooling beyond basic code completion

Core Competencies

Demonstrates expertise in designing, developing, and implementing machine learning applications and systems, with a strong focus on architectural design, model optimization, and cloud-based technologies. Proficient in building data pipelines and applying best practices in machine learning and software development.

Highest-signal resume keywords

• Machine Learning Application Development
• Python Programming
• Cloud-Based Architecture
• Data Pipeline Construction
• Agile Methodologies

Hard Skills

• Machine Learning
• Model Optimization
• Data Preparation
• Hyperparameter Tuning
• Continuous Integration
• Continuous Deployment
• Distributed Computing
• Model Validation
• Code Review
• Test Automation

Soft Skills

• Collaboration
• Problem Solving
• Leadership

Industry Keywords

• Responsible AI
• Explainable AI
• Big Data
• Data Science
• Machine Learning Frameworks

Tools & Technologies

• AWS
• Azure
• Google Cloud Platform
• Scikit-learn
• PyTorch
• Dask
• Spark
• TensorFlow