Machine Learning Engineer 5, Senior Manager - Intelligent Foundations and Experiences (IFX)

Capital One • McLean, VA

1d • $209,000 - $262,400

About The Position

Capital One is seeking a Machine Learning Engineer to help drive AI transformations and scale production machine learning models. The role combines aspects of operations, modeling, and data engineering, and involves designing, building, and delivering machine learning models and components that solve business problems in collaboration with Product and Data Science teams. The engineer may also build and scale multi-tenant platforms for large-scale model training and serving.

The position includes developing and testing application code, building and validating models, automating tests and deployments, contributing to cross-functional Agile teams, and retraining, maintaining, and monitoring production models. The engineer will use cloud architectures and platforms, construct data pipelines, and apply continuous integration and deployment practices. The role also emphasizes secure, well-managed code, risk-governed models, and Responsible and Explainable AI.

Requirements

• Bachelor's degree or higher in Computer Science, Machine Learning, or a related quantitative field such as Statistics, Economics, Operations Research, Analytics, Mathematics, or Engineering.
• At least 6 years of programming experience with Python, Java, Golang, or C++.
• At least 6 years of machine learning experience using industry-standard frameworks such as PyTorch or TensorFlow and libraries such as Pandas, NumPy, and Scikit-learn.
• At least 6 years of experience using and operating large-scale distributed systems such as Spark or Ray to prepare AI or machine learning data.
• At least 4 years of experience deploying and operating machine learning solutions in production, operating production services in the cloud (AWS, GCP, or Azure), and using Kubernetes to manage large-scale containerized machine learning systems.

Nice To Haves

• Master's or doctoral degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
• At least 2 years of experience building production RAG pipelines, including chunking, embeddings, vector indexing, graph RAG, or fine-tuning.
• At least 2 years of experience leveraging agentic patterns such as tool calling, ReAct loops, Plan-and-Execute, or multi-agent collaboration.
• At least 2 years of experience with LLM orchestration frameworks such as LangChain, LangGraph, LlamaIndex, or Semantic Kernel and model APIs such as OpenAI, Anthropic, Bedrock, Ollama, or Hugging Face.
• At least 2 years of experience building agentic AI systems and workflows.
• At least 2 years of experience developing AI and machine learning algorithms or technologies, such as LLM inference, similarity search and vector databases, guardrails, or memory, using Python, C++, C#, Java, CUDA, or Golang.
• At least 2 years of experience right-sizing models, instance counts, and hardware types to meet requirements such as context length and input and output token volumes.
• At least 5 years of experience optimizing machine learning algorithms, configurations, and infrastructure.
• At least 5 years of experience following software development best practices, including source control, testing, code reviews, and CI/CD.
• At least 5 years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans.
• At least 5 years of experience with machine learning techniques, model types and architectures, training concepts, and evaluating model accuracy and diagnosing common issues.
• At least 5 years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating machine learning models.
• Machine learning 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.

Responsibilities

• Design, build, and deliver machine learning models and components to solve real-world business problems in collaboration with Product and Data Science teams.
• Build and scale multi-tenant platforms for large-scale machine learning model training and serving.
• Use knowledge of machine learning modeling techniques and issues to inform infrastructure decisions, including model, data, and feature selection, training, hyperparameter tuning, dimensionality, bias and variance, and validation.
• Write and test application code, develop and validate machine learning models, and automate tests and deployment.
• Collaborate with cross-functional Agile teams to create and enhance software for big data and machine learning applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized machine learning models at scale.
• Construct optimized data pipelines to feed machine learning models.
• Use continuous integration and continuous deployment best practices, including test automation and monitoring, to deploy models and application code successfully.
• Manage code to reduce vulnerabilities, govern models from a risk perspective, and apply Responsible and Explainable AI best practices.

Benefits

• Comprehensive, competitive, and inclusive health, financial, and other benefits that support total well-being.
• Eligibility for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior