Machine Learning Engineer 5 (IC)

Capital One; McLean (VA); On-site; USD 140,000 - 200,000; Full time

Job summary

Capital One in McLean, VA is seeking a Machine Learning Engineer 5 (IC) to design, build, and deploy scalable ML models in collaboration with product and data science teams.

You will scale multi-tenant platforms, build data pipelines, and apply modern ML techniques in cloud environments (AWS/GCP/Azure) while adhering to responsible AI practices.

Qualifications

• Bachelor's degree or higher in Computer Science, ML, or related quantitative field.
• At least 6 years of Python, Java, Golang, or C++ development.
• At least 6 years of ML experience with PyTorch or TensorFlow and Pandas, NumPy, Scikit-learn.
• At least 6 years operating large-scale distributed systems (Spark, Ray) for AI data.
• At least 4 years deploying ML solutions in production and cloud (AWS, GCP, Azure) with Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams
• Scale multi-tenant platforms for large-scale ML training and serving
• Select data, features, and model types; tune hyperparameters and validate models
• Develop production-ready code and automate tests and deployment
• Collaborate in an Agile team to create big data and ML applications
• Retrain, monitor, and maintain models in production
• Leverage cloud architectures to deliver optimized ML models at scale
• Build data pipelines for feeding ML models
• Apply CI/CD and monitoring to ensure reliable model deployments
• Ensure code governance and Responsible & Explainable AI

Education

Bachelor's Degree or higher in Computer Science or related quantitative field

Master's Degree in Computer Science or related field

Doctoral Degree in Computer Science or related field

Tools

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

AWS

GCP

Azure

Job description

Machine Learning Engineer 5 (IC)

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.

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
• Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale
• 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 6 years of experience programming with Python, Java, Golang, or C++
• At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit‑learn)
• At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data
• At least 4 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
• 5+ years of experience optimizing ML algorithms, configurations, and infrastructure
• 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc.
• 5+ 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
• 5+ 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)
• 5+ years of experience designing, implementing, and scaling production‑ready data pipelines for training and evaluating ML models
• ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
• Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

The minimum and maximum full‑time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part‑time roles will be prorated based upon the agree