Machine Learning Engineer 4

Capital One; Cambridge (MA); On-site; USD 140,000 - 190,000; Full time

Job summary

Capital One seeks a Machine Learning Engineer 4 in Cambridge to advance AI transformations. You will design, deploy, and monitor scalable ML models, collaborate with product and data science teams, and leverage cloud platforms for real-time decisioning.

The role emphasizes building reliable pipelines, governance, and responsible AI practices while advancing cutting-edge open source frameworks and production systems.

Qualifications

• Bachelor’s degree or higher in CS, ML, or quantitative field.
• 4+ years programming in Python, Java, Golang, or C++.
• 4+ years ML experience with PyTorch or TensorFlow and data libraries.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure choices: data, features, training, and validation.
• Write and test application code; validate ML models; automate tests and deployment.
• Collaborate in a cross-functional Agile team to enable big data and ML apps.
• Retrain, monitor, and maintain models in production.
• Leverage cloud architectures to scale ML models; build data pipelines.
• Practice CI/CD, test automation, and monitoring for reliable deployments.
• Ensure code quality and model governance; adhere to Responsible AI practices.
• Use Python, Java, Golang, or C++ in production systems.

Skills

Python

Java

Golang

C++

Education

Bachelor's Degree or higher in Computer Science, ML or related field

Tools

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

AWS

GCP

Azure

Job description

Machine Learning Engineer 4

Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One.

In Risk Tech, we provide the foundation for Capital One to thrive in an uncertain world. Our engaged, empowered, and intelligent people produce outstanding products, working toward the common goal of transforming risk management with technology. We build data-driven tools that use machine learning to prevent risks & automatically detect issues before they impact our customers, our business, or our communities.

In this role at Risk Tech, you will work with our GRC team and partners across the company to build and deploy proprietary solutions for Risk management that are powered by state-of-the-art AI technology. Our products, enhanced with the transformative power of AI, are central to our business and deliver tremendous customer value.

What You'll Do:

• Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams


• Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model 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 as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications


• Retrain, maintain, and monitor models in production


• Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale


• Construct optimized data pipelines to feed ML models


• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code


• Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI


• Use programming languages like Python, Scala, or Java

Basic Qualifications:

• Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)


• At least 4 years of experience programming with Python, Java, Golang, or C++


• At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn)


• At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data


• At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems

Preferred Qualifications:

• Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field


• 3+ years of experience optimizing ML algorithms, configurations, and infrastructure


• 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc.


• 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans.


• 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting)


• 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.


• 1+ years of experience as a technical lead developing