Manager, Machine Learning Engineer

About The CompanyVanguard is a globally recognized leader in investment management, committed to delivering long-term financial well-being for its clients. With a mission to transform lives through innovative products and services, Vanguard emphasizes a culture of continuous learning, collaboration, and integrity. Operating across various regions from Malvern to Melbourne, the company fosters an environment where employees are empowered to grow professionally while contributing to impactful financial solutions. Vanguard’s dedication to client-centric strategies and operational excellence positions it as a trusted partner in wealth management and investment research.About The RoleThis role offers an exciting opportunity to join Vanguard’s dynamic team supporting model development and operational excellence within research and insights across Investment Management. The successful candidate will work closely with quantitative researchers, data scientists, and investment teams to engineer, deploy, and maintain production-grade machine learning models that drive research, analytics, and strategic business insights. The position involves managing the entire ML lifecycle—from building scalable pipelines and feature engineering workflows to automating deployment processes and monitoring models in production environments. Expertise in cloud-native architectures, particularly AWS SageMaker, and proficiency in Python are essential to succeed in this role. The ideal candidate will demonstrate a strong foundation in software engineering principles and hands-on experience implementing MLOps best practices to ensure reliable, scalable, and cost-efficient machine learning solutions.QualificationsMinimum of eight years of relevant work experience, including at least three years in a development-focused role.Undergraduate degree or equivalent experience; a graduate degree is preferred.Proven experience in software engineering, machine learning engineering, or data engineering within production environments.Deep expertise in Python and modern data science libraries such as Pandas, NumPy, Scikit-Learn, PyTorch, TensorFlow, or similar frameworks.Hands-on experience with AWS services, particularly AWS SageMaker, for model training, deployment, and management.Strong knowledge of building and maintaining end-to-end ML pipelines, feature engineering workflows, and automated deployment processes.Familiarity with MLOps practices, including CI/CD pipelines, model versioning, experiment tracking, monitoring, and automated retraining.Understanding of the software development lifecycle, testing strategies, and production support.Ability to collaborate effectively with researchers, data scientists, and business stakeholders to deliver actionable business outcomes.ResponsibilitiesDesign, develop, and maintain comprehensive machine learning pipelines that span research, validation, and production deployment.Engineer scalable workflows for training, inference, and model retraining using AWS SageMaker and other cloud-native tools.Create and maintain robust feature engineering, feature storage, and data preparation pipelines to support model development.Automate deployment, testing, validation, and release processes utilizing CI/CD practices to ensure seamless model updates.Develop batch, real-time, and event-driven architectures to support diverse ML use cases.Implement comprehensive model monitoring solutions to track performance, detect data or concept drift, and ensure operational health.Collaborate closely with quantitative researchers and data scientists to operationalize research models effectively.Manage model versioning, lineage, experiment tracking, and ensure reproducibility of machine learning workflows.Optimize model performance, scalability, reliability, and cloud resource utilization to reduce costs and improve efficiency.Establish engineering standards, testing frameworks, and governance controls to uphold quality and compliance in ML solutions.Support ongoing production operations, incident response, and continuous improvement initiatives for deployed models.BenefitsVanguard offers a comprehensive benefits package designed to support the well-being and professional growth of its employees. This includes competitive compensation, health insurance plans, retirement savings options, and wellness programs. Employees have access to ongoing training and development opportunities, fostering continuous learning and career advancement. The company promotes a flexible hybrid working environment, enabling team members to balance in-person collaboration with remote work. Additionally, Vanguard encourages a culture of inclusion and diversity, providing a supportive environment where all employees can thrive and contribute meaningfully to the organization’s mission.Equal OpportunityVanguard is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, religion, gender, sexual orientation, age, disability, or any other protected characteristic. Our hiring practices are designed to attract and retain talented individuals from diverse backgrounds, ensuring a vibrant and innovative workforce that reflects the communities we serve.