About The Company
Capital One is a leading diversified bank renowned for its innovative approach to financial services and commitment to customer-centric solutions. With a strong emphasis on leveraging technology and data-driven insights, Capital One has established itself as a pioneer in digital banking, credit cards, and lending solutions. The company prides itself on fostering a collaborative and inclusive work environment that encourages continuous learning, innovation, and professional growth. Operating across multiple regions, Capital One strives to deliver exceptional value to its customers while maintaining the highest standards of integrity and responsibility.
About The Role
The Lead Machine Learning Engineer at Capital One plays a pivotal role in designing, developing, and deploying scalable machine learning solutions that drive business insights and operational efficiency. This position involves working within an Agile team dedicated to productionizing machine learning applications, ensuring high availability, robustness, and performance. The successful candidate will be responsible for architecting ML systems, developing and reviewing model and application code, and implementing best practices in responsible AI. This role offers a unique opportunity to work at the forefront of technological innovation, utilizing cloud platforms such as AWS and Kubernetes to deliver impactful ML solutions at scale. The Lead ML Engineer will collaborate closely with cross-functional teams, including Data Science, Data Engineering, and Product Management, to translate complex data problems into actionable, scalable solutions that meet business needs.
Qualifications
The ideal candidate will possess a strong educational background with a Bachelor's Degree in Computer Science, Electrical Engineering, Mathematics, or a related field. A minimum of six years of experience in designing and building data-intensive solutions using distributed computing frameworks is required. Candidates should have at least four years of programming experience with Python, Scala, or Java, along with a minimum of two years of experience building, scaling, and optimizing machine learning systems. Preferred qualifications include a Master's or Doctoral degree, extensive experience with industry-recognized ML frameworks such as TensorFlow, PyTorch, or Spark, and a proven track record of developing production-ready data pipelines. Leadership experience, particularly in managing teams developing ML solutions, is highly valued. Experience deploying ML solutions on cloud platforms such as AWS, Azure, or Google Cloud, and familiarity with Kubernetes infrastructure, are also preferred. The candidate should demonstrate a strong understanding of ML modeling techniques, data governance, and responsible AI practices.
Responsibilities
The Lead Machine Learning Engineer will be responsible for a broad range of activities, including but not limited to designing and building machine learning models that address real-world business challenges in collaboration with Data Science and Product teams. They will inform infrastructure decisions based on ML modeling techniques, including model selection, feature engineering, hyperparameter tuning, and validation. Developing, testing, and deploying application code that automates model training, validation, and monitoring is essential. The role involves constructing and optimizing data pipelines to feed ML models efficiently and reliably. Ensuring models are retrained and maintained in production environments, with continuous monitoring for performance and bias, is critical. The engineer will leverage cloud-based architectures, utilizing AWS, Kubernetes, and other platforms to deliver scalable solutions. They will implement best practices in CI/CD, including automated testing, vulnerability management, and model governance to uphold ethical AI standards. Additionally, the role entails collaborating with cross-functional teams to enhance existing systems, troubleshoot issues, and implement innovative solutions. The candidate will also contribute to industry thought leadership through conference presentations, publications, or open-source contributions, demonstrating impact in the ML community.
Benefits
Capital One offers a comprehensive and competitive benefits package designed to support the overall well-being of its employees. Benefits include health insurance options, retirement plans, paid time off, and wellness programs. The company also provides opportunities for professional development, continuous learning, and career advancement. Employees have access to resources such as mentorship programs, technical training, and participation in industry conferences. Additionally, Capital One promotes a flexible work environment that values diversity and inclusion, fostering a culture where all employees can thrive. Incentive programs, including performance-based bonuses and long-term incentives, are available to recognize contributions and drive motivation. The organization is committed to creating an equitable workplace and supporting the holistic health of its team members.
Equal Opportunity
Capital One is an equal opportunity employer committed to fostering an inclusive environment for all employees and applicants. The company does not discriminate based on race, color, religion, sex, national origin, age, disability, veteran status, or any other protected characteristic under applicable law. Capital One values diversity and is dedicated to providing equal employment opportunities to qualified individuals. The organization also maintains a drug-free workplace and complies with all applicable federal, state, and local laws regarding non-discrimination and fair employment practices. Applicants requiring accommodations during the recruitment process are encouraged to contact the company’s recruiting team to ensure a fair and accessible hiring experience.