Director, Machine Learning Engineer

Own and scale production machine learning that tackles complex financial services challenges at Capital One. This onsite role in McLean, VA brings together engineering leadership, cloud and distributed systems, and hands-on model delivery to build state-of-the-art intelligent systems. You will lead large-scale ML initiatives, optimize the full lifecycle from data pipelines to inference, and help mentor engineering talent as a force-multiplier.

Compensation: $269,100 - $307,200 per year (McLean, VA). Performance-based incentive compensation may include cash bonus(es) and/or long term incentives (LTI).

What you’ll do

• Deliver machine learning models and software components that solve challenging business problems in financial services, partnering with Product, Architecture, Engineering, and Data Science
• Drive the creation and evolution of ML models and software for intelligent systems
• Lead large-scale ML initiatives with the customer in mind
• Leverage cloud-based architectures and technologies to deliver optimized models at scale
• Optimize data pipelines that feed ML models
• Use programming languages such as Python, Scala, or Java
• Evangelize best practices across engineering and modeling lifecycles
• Recruit, nurture, and retain top engineering talent, balancing deep hands-on work with mentoring and elevating peers and junior engineers
• Stay current with technology trends through experimentation, learning, and participation in internal and external technology communities, including mentoring others

Core qualifications

• Bachelor’s Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• 3+ years of people leadership experience
• 8+ years of experience programming with Python, Java, Golang, or C++
• 6+ years of machine learning experience using industry-standard frameworks PyTorch or Tensorflow and libraries including Pandas, NumPy, Scikit-learn
• 6+ years using and operating large-scale distributed systems such as Spark and Ray to prepare AI or machine learning data
• 5+ years deploying and operating production machine learning solutions and production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large-scale containerized ML software

Preferred qualifications

• Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or a related field
• 5+ years managing and leading an engineering team
• 3+ years architecting and designing resilient, large-scale production machine learning systems from data preparation to model training and inference
• 5+ years optimizing ML algorithms, configurations, and infrastructure
• 5+ years with ML techniques (Supervised, semi-supervised, unsupervised, reinforcement learning, etc.), model types (Regression, Classification, Clustering, etc.), and architectures (RNNs, CNNs, LSTMs, Transformers), including training concepts (loss function, hyperparameters, regularization) and diagnosing issues such as underfitting and overfitting
• 7+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
• Ability to communicate complex technical concepts clearly to a variety of audiences
• Experience driving ML impact through conference presentations, papers, blog posts, open source contributions, or patents
• Experience hiring and developing high-performing ML engineers with an inspiring leadership style
• Highly developed interpersonal, presentation, and communications skills

Benefits

• Performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
• Comprehensive, competitive, and inclusive benefits supporting your total well-being (eligibility varies by full-time/part-time status, exempt/non-exempt status, and management level)

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

Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe, and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).