Senior Manager, Machine Learning Engineer (People Leader)

Capital One is seeking a Senior Manager, Machine Learning Engineer (People Leader) to lead machine learning engineering efforts and contribute hands-on across the lifecycle of production ML systems. The role is based in Plano, TX (onsite) and includes technical leadership, mentoring, and talent development.

Role Overview

The Senior Manager, Machine Learning Engineer (People Leader) will design, build, deploy, monitor, and govern machine learning solutions at scale. You will also help shape ML infrastructure decisions, improve production operations through automation and monitoring, and ensure models are managed with appropriate risk and governance practices aligned with Responsible and Explainable AI.

Responsibilities

• Design, build, and/or deliver ML models and components that address real 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
• Apply ML modeling expertise to guide infrastructure and technical decisions, including model choice, data and feature selection, training approaches, hyperparameter tuning, dimensionality considerations, bias/variance tradeoffs, and validation
• Address complex technical challenges through application development, ML model development and validation, and automation of tests and deployments
• Collaborate in a cross-functional Agile environment to create and enhance software for big data and ML applications
• Retrain, maintain, and monitor ML models in production
• Leverage and/or build cloud-based architectures and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines to support ML model training and operations
• Use CI/CD best practices, including test automation and monitoring, to support reliable deployment of ML models and application code
• Maintain secure, well-managed code to reduce vulnerabilities, apply risk-aware governance for models, and follow best practices in Responsible and Explainable AI
• Serve as a force-multiplier by balancing deep technical contribution and innovation with mentoring and skill elevation for peers and junior engineers
• Recruit, nurture, and retain engineering talent
• Stay current with technology trends through experimentation and participation in internal and external technology communities while mentoring others

Required 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 industry standard frameworks PyTorch or Tensorflow and libraries including Pandas, NumPy, and Scikit-learn
• At least 6 years of experience using and operating large-scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data
• At least 4 years of experience deploying and operating production ML solutions and production services in the cloud (AWS, GCP, Azure), using Kubernetes for large-scale containerized ML systems
• At least 1 year of people leadership experience

Technologies

• Python, Java, Golang, C++
• PyTorch, Tensorflow
• Pandas, NumPy, Scikit-learn
• Spark, Ray
• AWS, GCP, Azure
• Kubernetes
• CI/CD
• Responsible and Explainable AI

Preferred Qualifications

• Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
• 3+ years of experience managing and leading an ML engineering team
• 5+ years of experience optimizing ML algorithms, configurations, and infrastructure
• 5+ years of experience applying software development best practices including source control, testing, code reviews, and CI/CD
• 5+ years of experience building resilient software solutions, including pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring and alarms, and preparing incident response plans
• 5+ years of experience across ML techniques including supervised, semi-supervised, unsupervised, and reinforcement learning, along with model types (Regression, Classification, Clustering) and architectures (RNNs, CNNs, LSTMs, Transformers)
• 5+ years of experience with training concepts including loss function, hyperparameters, regularization, and evaluating model accuracy and diagnosing issues such as underfitting and overfitting
• 5+ years of 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

Compensation

• Plano, TX (onsite): USD 209,000 - 238,500 per year
• McLean, VA: USD 229,900 - 262,400 for Sr. Manager, Machine Learning Engineer
• New York, NY: USD 250,800 - 286,200 for Sr. Manager, Machine Learning Engineer
• San Francisco, CA: USD 250,800 - 286,200 for Sr. Manager, Machine Learning Engineer

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

Additional Information

• Capital One will consider sponsoring a new qualified applicant for employment authorization for this position
• Applications are expected to remain open for a minimum of 5 business days
• No agencies please
• Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws
• Capital One promotes a drug-free workplace
• Capital One will consider qualified applicants with a criminal history in a manner consistent with applicable laws regarding criminal background inquiries
• If you need an accommodation for applying or finding information, contact Capital One Recruiting at 1-800-304-9102 or [email protected]
• For technical support or questions about Capital One’s recruiting process, email [email protected]
• Capital One does not provide, endorse, or guarantee third-party products, services, educational tools, or other information available through this site
• Capital One Financial is made up of several different entities (Canada, United Kingdom, Philippines)