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

Capital One seeks Machine Learning Engineers to design, build, and scale machine learning models and platforms for production business use cases.

Responsibilities

• Design, build, and/or deliver ML models and components that address real-world business problems in collaboration with Product and Data Science
• Build and scale massive multi-tenant platforms for large-footprint ML training and/or serving at scale
• Guide ML infrastructure choices using expertise in modeling and delivery topics including model selection, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployments
• Contribute in a cross-functional Agile team to create and enhance software for big data and ML applications
• Retrain, maintain, and monitor ML models in production
• Use and/or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines that feed ML models
• Apply CI/CD best practices including test automation and monitoring to support successful deployment of ML models and application code
• Manage code to reduce vulnerabilities, govern models from a risk perspective, and follow Responsible and Explainable AI best practices
• Use programming languages such as Python, Scala, or Java

Requirements

• Bachelor’s Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• 4+ years minimum experience (role level)
• 6+ years programming with Python, Java, Golang, or C++
• 6+ years ML experience using industry standard frameworks PyTorch or TensorFlow plus libraries (Pandas, NumPy, Scikit-learn)
• 6+ years working with and operating large-scale distributed systems (Spark, Ray) to prepare AI or ML data
• 4+ years deploying and operating ML solutions in production and operating production services in the cloud (AWS, GCP, Azure), using Kubernetes to manage large-scale containerized ML systems

Technology Stack

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

Benefits

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

Preferred Qualifications

• Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or 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 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 given requirements (context length, token inputs, token outputs)
• 5+ years experience optimizing ML algorithms, configurations, and infrastructure
• 5+ years experience following software development best practices including source control, testing, code reviews, and CI/CD
• 5+ years experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plans
• 5+ years experience with ML techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning) and model types (Regression, Classification, Clustering) plus architectures (RNNs, CNNs, LSTMs, Transformers), including training concepts (loss function, hyperparameters, regularization) and diagnosing underfitting/overfitting
• 5+ years experience 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

Salary Information

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

Location

• McLean, VA (onsite)