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

In this senior leadership Machine Learning Engineer role, you will help design and scale AI and machine learning capabilities, from model development through production training and monitoring. The position supports Capital One-focused implementation work and involves building multi-tenant ML platforms alongside Product and Data Science teams.

This onsite role is based in McLean, VA and targets candidates with strong experience across ML modeling, distributed data systems, and cloud deployment practices.

Role focus

• Design, build, and deliver ML models and components to address business problems in collaboration with Product and Data Science teams
• Build and scale large multi-tenant platforms for ML model training and serving at scale
• Use ML modeling knowledge to guide infrastructure decisions, including model choice, data and feature selection, training approach, hyperparameter tuning, dimensionality, and bias/variance tradeoffs and validation
• Solve complex engineering problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Retrain, maintain, and monitor models in production
• Leverage or build cloud-based 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 best practices, including test automation and monitoring, to support successful deployment of ML models and application code
• Maintain well-managed code to reduce vulnerabilities, ensure risk-governed model usage, and follow best practices in Responsible and Explainable AI
• Work within an Agile, cross-functional team to create and enhance software enabling big data and ML applications

Programming and tooling

• Use Python, Scala, or Java for development
• Technologies include PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn
• Distributed systems for data preparation include Spark and Ray
• Cloud and orchestration: AWS, GCP, Azure, and Kubernetes
• CI/CD practices are expected for deployment workflows

Minimum 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 PyTorch or Tensorflow and libraries such as Pandas, NumPy, and Scikit-learn
• At least 6 years using and operating large scale distributed systems like Spark and Ray for AI or ML data preparation
• At least 4 years deploying and operating ML solutions in production, operating production services in the cloud (AWS, GCP, Azure), and using Kubernetes to manage large scale containerized ML systems

Preferred qualifications

• Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or a related field
• +2 years experience building production RAG pipelines (chunking, embeddings, vector indexing, graph RAG, or fine-tuning)
• +2 years experience leveraging agentic patterns (Tool Calling, ReAct loops, Plan-and-Execute, Multi-Agent Collaboration)
• +2 years with LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel) and model APIs (OpenAI, Anthropic, Bedrock, Ollama, Hugging Face)
• +2 years of experience building agentic AI systems and agentic workflows
• +2 years developing AI and ML algorithms or technologies (e.g., LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang
• +2 years experience right-sizing models, instance counts, and hardware types based on requirements (context length, token inputs, token outputs)
• 5+ years optimizing ML algorithms, configurations, and infrastructure
• 5+ years following software development best practices including source control, testing, code reviews, and CI/CD
• 5+ years building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response planning
• 5+ years working with ML techniques (Supervised, semi-supervised, and unsupervised learning, reinforcement learning) and model types (Regression, Classification, Clustering)
• 5+ years with model architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and evaluating model accuracy
• 5+ years 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 to a variety of audiences

Compensation and benefits

• Salary: USD 229,900 - 262,400 per year for the McLean, VA location (Machine Learning Engineer 5)
• Salary: USD 209,000 - 238,500 per year for Richmond, VA (Machine Learning Engineer 5)
• Eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI)
• Capital One offers comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being