Senior ML Engineer - AI at Scale & Production

TalentAlly; Cambridge (MA); On-site; USD 197,000 - 225,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer 4 to drive AI transformations and scale production models within MLX. You will design, train, and deploy ML models, collaborate with product and data science teams, and build robust data pipelines and cloud-based ML infrastructure.

The role emphasizes responsible AI practices, CI/CD for ML, and working across cross-functional teams to deliver scalable, production-ready solutions in a dynamic environment.

Qualifications

• Bachelor's Degree or higher in Computer Science, Machine Learning or related quantitative field.
• 4+ years of experience programming with Python, Java, Golang, or C++.
• 4+ years of ML experience with PyTorch or TensorFlow and Pandas/NumPy/Scikit-learn.
• 4+ years of experience with large-scale distributed systems (Spark, Ray).
• 2+ years deploying ML in production and cloud (AWS, GCP, Azure) with Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure decisions including model/data/feature choices and validation.
• Develop and test application code, ML models, and deployment automation.
• Collaborate in an Agile cross-functional team to enable state-of-the-art ML applications.
• Retrain, monitor, and maintain models in production.
• Build and optimize data pipelines for ML training and evaluation.
• Apply CI/CD and test automation for reliable deployments.
• Ensure governance and Responsible AI practices in code and models.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

Education

Bachelor's degree or higher in CS/ML or related

Master's or Doctoral degree in CS/EE/Math or related

Tools

Spark

Ray

Kubernetes

CI/CD tools

Job description

Capital One is seeking a Machine Learning Engineer 4 to drive AI transformations and scale production models within MLX. You will design, train, and deploy ML models, collaborate with product and data science teams, and build robust data pipelines and cloud-based ML infrastructure.

The role emphasizes responsible AI practices, CI/CD for ML, and working across cross-functional teams to deliver scalable, production-ready solutions in a dynamic environment.