Lead Machine Learning Engineer

Center 1 (19052), United States of America, McLean, Virginia Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to turning machine learning applications and systems into production at scale. You’ll participate in the granular technical design, development, and execution of machine learning applications using current and emerging technological platforms. You’ll concentrate on machine learning architectural design, develop and review model and application code, and ensure our machine learning applications maintain high availability and performance. You'll get the chance to continually learn and apply the most innovative and best practices in the area of machine learning engineering. By joining the Capital One Travel Decisioning group, you'll get the opportunity to construct machine learning models that will be at the heart of providing our customers with personalized recommendations, offers, and travel experiences. What you’ll do in the role: The MLE role overlaps with many disciplines such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many machine learning engineering activities, which may include one or more of the following: - Design, develop, and/or roll out ML models and components that address real-world business issues, while collaborating with the Product and Data Science teams. - Make informed ML infrastructure decisions using your comprehension of ML modeling techniques and problems, including model choice, data and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation. - Solve challenging problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. - Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and machine learning applications. - Retrain, maintain, and monitor models in production. - Employ or construct cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. - Construct optimized data pipelines to feed ML models. - Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. - Ensuring all code is well managed to reduce vulnerabilities, models are well governed from a risk perspective, and that the ML follows Responsible and Explainable AI best practices. - Use programming languages like Python, Scala, or Java. Basic Qualifications: - Bachelor’s degree - At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience is not applicable) - At least 4 years of experience programming with Python, Scala, or Java - At least 2 years of experience building, scaling, and optimizing ML systems Preferred Qualifications: - Master's or doctoral degree in computer science, electrical engineering, mathematics, or similar field - 3+ years of experience building production-ready data pipelines feeding ML models - 3+ years of professional experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow - 2+ years of experience developing performant, resilient, and maintainable code - 2+ years of experience with data gathering and preparation for ML models - 1+ years of experience leading teams in developing ML solutions using industry best practices, patterns, and automation - Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform - Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance - Industry impact through conference presentations, papers, blog posts, contribution in open source, or patents At this time, Capital One will not sponsor a new applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to carry out work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon regular working hours. New York City (Hybrid On-Site): $197,400 - $225,300 for Lead Machine Learning Engineer Candidates hired to work in other locations will receive the pay range associated with that location, and the actual annual salary offered to any candidate at the time of hiring will be reflected solely in the candidate’s offer letter. This role is also eligible to earn performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI). Incentives could be discretionary or non-discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.