Lead Machine Learning Engineer

About the role

About The Company

Capital One is a leading diversified financial services company renowned for its innovative approach to banking and credit solutions. With a strong commitment to leveraging technology and data-driven insights, Capital One strives to deliver exceptional customer experiences and develop cutting-edge financial products. The company operates across various segments including credit cards, auto loans, savings accounts, and commercial banking, serving millions of customers nationwide. Known for fostering a collaborative and inclusive workplace culture, Capital One emphasizes continuous learning, innovation, and responsible banking practices. Its dedication to diversity, equity, and inclusion has positioned it as a respected employer in the financial industry, committed to making a positive impact in the communities it serves.

About The Role

As a Lead Machine Learning Engineer at Capital One, you will play a pivotal role in transforming data into strategic insights and scalable machine learning solutions that drive business growth. You will be part of an agile, cross-functional team dedicated to deploying high-quality ML applications at scale. Your expertise will be essential in designing, developing, and operationalizing sophisticated machine learning models and systems that address complex business challenges. The role offers an exciting opportunity to work with the latest technological advancements, cloud-based architectures, and industry best practices to build resilient, efficient, and explainable AI solutions. You will lead initiatives to optimize data pipelines, enhance model performance, and ensure the responsible deployment of AI technologies, all while collaborating with diverse teams across the organization.

Qualifications

The ideal candidate will possess a strong educational background and extensive practical experience in data science and engineering. A bachelor’s degree in computer science, electrical engineering, mathematics, or a related field is required, with advanced degrees preferred. Candidates should have at least six years of experience designing and building data-intensive solutions using distributed computing frameworks, with a minimum of four years programming proficiency in Python, Scala, or Java. Additionally, candidates must have at least two years of experience in developing, scaling, and optimizing machine learning systems. Proven experience in building production-grade data pipelines, deploying ML models, and working with cloud platforms such as AWS, Azure, or Google Cloud is highly desirable. Strong understanding of ML frameworks like TensorFlow, PyTorch, or scikit-learn, along with knowledge of responsible AI practices, will be advantageous.

Responsibilities

Design, develop, and deploy scalable machine learning models and components that address real-world business problems in collaboration with product and data science teams.

Make informed decisions regarding ML infrastructure by applying expertise in model selection, feature engineering, hyperparameter tuning, and validation techniques.

Write, test, and optimize application code to solve complex problems, automate deployment processes, and improve system resilience.

Collaborate within agile teams to create innovative software solutions that leverage big data and advanced ML techniques.

Maintain and monitor models in production environments, ensuring high performance, accuracy, and compliance with governance standards.

Leverage cloud platforms and build data pipelines to support ML workflows, ensuring high throughput and low latency.

Implement continuous integration and continuous deployment (CI/CD) practices, including automated testing and monitoring, to facilitate seamless model updates and deployments.

Ensure all code and models adhere to security standards, risk management policies, and responsible AI principles, including explainability and fairness.

Utilize programming languages such as Python, Scala, or Java to develop robust and maintainable solutions.

Lead efforts in mentoring team members, sharing best practices, and driving innovation within the ML engineering domain.

Benefits

Capital One offers a comprehensive benefits package designed to support your overall well-being and professional growth. Employees are eligible for competitive health insurance plans, including medical, dental, and vision coverage. The company provides financial benefits such as retirement plans, stock options, and performance-based incentives, including cash bonuses and long-term incentives. Capital One promotes work-life balance through flexible work arrangements, paid time off, and wellness programs. Employees also have access to ongoing training and development opportunities, including certifications, workshops, and conferences, to stay ahead in the rapidly evolving field of machine learning. The organization fosters an inclusive environment that values diversity and encourages innovation, making it an ideal place for professionals seeking meaningful and impactful careers in technology and finance.

Equal Opportunity

Capital One is an equal opportunity employer committed to fostering a diverse and inclusive workplace. We do not discriminate based on race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other protected characteristic under applicable law. We are dedicated to providing reasonable accommodations to qualified individuals with disabilities and ensuring a welcoming environment for all applicants and employees. Capital One's policies uphold compliance with all relevant federal, state, and local laws, and we actively promote a culture of respect, fairness, and equal opportunity in all aspects of employment.