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

Capital One is seeking a Senior Manager-level Machine Learning Engineer to design, build, and scale machine learning models and platforms that support real-world business needs. The role focuses on ML infrastructure, production data pipelines, and responsible and explainable AI across cloud environments.

Responsibilities

• Design, build, and/or deliver ML models and components that address real-world business problems in collaboration with Product and Data Science teams.
• Build and scale multi-tenant platforms that support large footprint ML training and/or serving at scale.
• Guide ML infrastructure decisions using expertise across model selection and data and feature selection, including training, hyperparameter tuning, dimensionality, bias/variance, and validation.
• Develop and validate ML models and application code, including automated testing and deployment.
• Collaborate within a cross-functional Agile team to create and enhance software for big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage and/or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Apply CI/CD best practices, including test automation and monitoring, to support successful deployment of ML models and application code.
• Ensure code is well-managed to reduce vulnerabilities; support risk-governed model practices 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).
• At least 6 years of experience programming with Python, Java, Golang, or C++.
• At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or TensorFlow, and libraries including Pandas, NumPy, and Scikit-learn.
• At least 6 years of experience operating large-scale distributed systems such as Spark and Ray to prepare AI and Machine Learning data.
• At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure), including using Kubernetes to manage large-scale containerized ML software 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 (for example, 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 (for example, context length, token inputs, token outputs).
• 5+ years of experience 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 with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring and alarms, and preparing incident response plans.
• 5+ years working with machine learning techniques (supervised, semi-supervised, unsupervised, reinforcement learning) and model types (regression, classification, clustering), including architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and evaluating accuracy and diagnosing issues (underfitting, 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.

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 set of health, financial, and other benefits to support total well-being.

Compensation and Location

Location: McLean, VA (onsite)

Salary: USD 229,900 - 262,400 per year

Salary information: McLean, VA: $229,900 - $262,400 for Machine Learning Engineer 5; Richmond, VA: $209,000 - $238,500 for Machine Learning Engineer 5