Senior Lead ML Engineer — Build Production-Ready AI

COMFORT SYSTEMS; Cambridge (MA); On-site; USD 230,000 - 262,000; Full time

Job summary

Capital One is seeking a Senior Lead Machine Learning Engineer to join an Agile team focused on productionizing machine learning applications at scale. You will design, build, and deploy ML models and infrastructure, collaborating with Product and Data Science teams to deliver scalable solutions.

You will lead ML engineering efforts across data pipelines, model training, validation, and deployment, with cloud architectures, CI/CD, and responsible AI practices.

Qualifications

• Bachelor’s Degree required.
• 8+ years designing and building data-intensive solutions.
• 4+ years programming Python/Scala/Java.
• 3+ years building, scaling ML systems.
• 2+ years leading teams delivering ML solutions.

Responsibilities

• Design, build, and deliver ML models and components with product and data science teams.
• Inform ML infrastructure decisions: model choice, data and feature selection, training, validation.
• Write and test application code, develop and validate ML models, automate tests and deployment.
• Collaborate in a cross-functional Agile team to enable big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage cloud architectures to deliver optimized ML models at scale.
• Construct data pipelines to feed ML models.
• Use CI/CD and monitoring to ensure successful deployment.
• Ensure code governance and responsible/Explainable AI practices.
• Use Python/Scala/Java for implementation.

Skills

Distributed computing

Python

Scala

Java

ML engineering

Team leadership

Education

Bachelor's degree

Tools

scikit-learn

PyTorch

Dask

Spark

TensorFlow

Job description

Capital One is seeking a Senior Lead Machine Learning Engineer to join an Agile team focused on productionizing machine learning applications at scale. You will design, build, and deploy ML models and infrastructure, collaborating with Product and Data Science teams to deliver scalable solutions.

You will lead ML engineering efforts across data pipelines, model training, validation, and deployment, with cloud architectures, CI/CD, and responsible AI practices.