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

Capital One is looking for Machine Learning Engineers to design, build, and scale machine learning models and platforms that address real-world business needs. The role focuses on production training and serving, ML infrastructure, data pipelines, and responsible, explainable AI across cloud environments.

Location

• McLean, VA (onsite)

Salary

• McLean, VA: USD 229,900 - 262,400 per year (Machine Learning Engineer 5)
• Richmond, VA: USD 209,000 - 238,500 per year (Machine Learning Engineer 5)

Core Responsibilities

• Design, build, and/or deliver ML models and components that solve real-world business problems while collaborating with Product and Data Science teams
• Build and scale massive multi-tenant platforms for large-footprint model training and/or serving
• Apply ML modeling knowledge to guide infrastructure decisions, including model selection, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance considerations, and validation
• Address complex problems through application code development, ML model development and validation, and automated testing and deployment
• Collaborate in a cross-functional Agile team to create and improve software enabling big data and ML applications
• Retrain, maintain, and monitor models in production
• Use or build cloud architectures and technologies to deliver optimized ML models at scale
• Construct optimized data pipelines to support ML model needs
• Apply CI/CD best practices including test automation and monitoring to support reliable ML model and code deployments
• Maintain well-governed code to reduce vulnerabilities and support risk governance, following best practices for Responsible and Explainable AI
• Use programming languages such as Python, Scala, or Java

Required Qualifications

• Education: Bachelor’s Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• Experience: At least 6 years programming with Python, Java, Golang, or C++
• Experience: At least 6 years in Machine Learning using industry standard frameworks PyTorch or Tensorflow, and libraries (Pandas, NumPy, Scikit-learn)
• Experience: At least 6 years using and operating large-scale distributed systems (Spark, Ray) to prepare AI/ML data
• Experience: 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

Technologies

• Python, Scala, Java, Golang, C++
• PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn
• Spark, Ray
• AWS, GCP, Azure
• Kubernetes

Preferred Qualifications

• Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or a related field
• +2 years building production RAG pipelines (chunking, embeddings, vector indexing, graph RAG, or fine-tuning)
• +2 years 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 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 right-sizing models, instance counts, and hardware types given requirements (e.g., 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, CI/CD, etc.
• 5+ years 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 working with ML techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.), model types (Regression, Classification, Clustering), model architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and evaluating model accuracy while diagnosing issues such as underfitting and overfitting
• 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 clearly to a variety of audiences

Benefits

• Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
• Comprehensive, competitive, and inclusive set of health, financial and other benefits supporting total well-being