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

Capital One • McLean, VA

9d

About The Position

Capital One is seeking a Machine Learning Engineer to help drive AI transformation and scale production models. The role combines machine learning engineering with operations, modeling, and data engineering, working with Product and Data Science teams and within cross-functional Agile teams. The engineer designs, builds, and delivers models and components for business problems; develops scalable, multi-tenant platforms for model training and serving; and makes infrastructure decisions informed by modeling techniques and data and feature selection. The role also involves developing and testing application code, validating models, automating tests and deployment, maintaining and monitoring production models, building cloud-based solutions and optimized data pipelines, and applying CI/CD practices. Code and models must be managed and governed responsibly, with attention to vulnerabilities, risk, Responsible AI, and Explainable AI. The position uses programming languages such as Python, Scala, or Java. The role is eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives. Salary ranges depend on location: $229,900–$262,400 in McLean, Virginia, or $209,000–$238,500 in Richmond, Virginia.

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 a cloud environment (AWS, GCP, or Azure), and using Kubernetes to manage large-scale containerized ML systems.

Nice To Haves

• Master’s or doctoral degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
• More than 2 years of experience building production RAG pipelines, including chunking, embeddings, vector indexing, graph RAG, or fine-tuning.
• More than 2 years of experience with agentic patterns such as tool calling, ReAct loops, Plan-and-Execute, or multi-agent collaboration.
• More than 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.
• More than 1 year of experience building agentic AI systems and workflows.
• More than 1 year of experience developing AI and ML algorithms or technologies such as LLM inference, similarity search and vector databases, guardrails, or memory using Python, C++, C#, Java, CUDA, or Golang.
• More than 2 years of experience right-sizing models, instance counts, and hardware types for requirements such as context length and token input and output volumes.
• 5 or more years of experience optimizing ML algorithms, configurations, and infrastructure.
• 5 or more years of experience following software development best practices, including source control, testing, code reviews, and CI/CD.
• 5 or more years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans.
• 5 or more years of experience with machine learning techniques, model types and architectures, training concepts, and evaluating model accuracy and diagnosing issues such as underfitting and overfitting.
• 5 or more years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML 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 varied audiences.

Responsibilities

• Design, build, and deliver machine learning models and components to solve 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.
• Inform ML infrastructure decisions using knowledge of modeling techniques, data and feature selection, training, hyperparameter tuning, dimensionality, bias and variance, and validation.
• Write and test application code, develop and validate ML models, and automate testing and deployment.
• Collaborate with cross-functional Agile teams to create and enhance software for big data and machine learning applications.
• Retrain, maintain, and monitor production models.
• Leverage or build cloud architectures, technologies, and platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines that feed ML models.
• Apply continuous integration and continuous deployment practices, including test automation and monitoring.
• Manage code to reduce vulnerabilities, govern models from a risk perspective, and follow Responsible and Explainable AI best practices.

Benefits

• Comprehensive health, financial, and other benefits supporting employee well-being.
• 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