Director, Machine Learning Cyber Engineer

Cyber Security Machine Learning Engineer Overview Cyber security is one of the biggest threats of our day. In this role, you will have the opportunity to do work that protects our company and our customers. We’re looking for someone who is passionate about cyber security and will help us achieve our business needs by driving delivery of Cyber tools and by integrating with software and processes across the company. Ensuring that Capital One’s Cyber Detection and Mitigation system capabilities are able to continually grow to address the newest/latest threats. As a Capital One Machine Learning Cyber Engineer, you'll be providing technical leadership to Agile teams dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. Working within an Agile environment, you’ll serve as a technical domain expert in machine learning, guiding machine learning architectural design decisions, developing and reviewing model and application code, and ensuring high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. You’ll also mentor other engineers and further develop your technical knowledge and skills to keep Capital One at the cutting edge of technology. What you’ll do in the role: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams. Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems. Own the business or technical vision, collaborate with large cross-functional teams, secure commitments on deliverables and unblock teams to land business impact Lead large-scale ML initiatives with the customer in mind. Leverage cloud-based architectures and technologies to deliver optimized ML models at scale. Optimize data pipelines to feed ML models. Use programming languages like Python, Scala, or Java. Evangelize best practices in all aspects of the engineering and modeling lifecycles. Attract and develop high-performing software engineers with an inspiring leadership style. Leverage your strong interpersonal, presentation/storytelling, and communications skills. Demonstrate Machine Learning industry impact through conference presentations, papers, blog posts, open source contributions, or patents.