Machine Learning Engineer 5 (IC)

About the role

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.

Responsibilities

• 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 ML infrastructure decisions using understanding of modeling techniques and issues, including choice of model, data, feature selection, training, hyper‑parameter 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, unsupervised, reinforcement learning, etc.), model types (regression, classification, clustering, etc.), model architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and evaluating model accuracy while diagnosing common issues (underfitting, overfitting).
• 5+ years of experience designing, implementing, and scaling production‑ready data pipelines for training and evaluating ML models.
• Demonstrated 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.

Pay

• McLean, VA: $229,900 – $262,400
• New York, NY: $250,800 – $286,200
• Richmond, VA: $209,000 – $238,500
• San Francisco, CA: $250,800 – $286,200
• San Jose, CA: $250,800 – $286,200

Candidates hired in other locations will receive the pay range associated with that location. The role is also eligible for performance‑based incentive compensation, which may include cash bonuses and/or long‑term incentives.

Benefits

Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support total well‑being. Eligibility varies based on full‑ or part‑time status, exempt or non‑exempt status, and management level.