Machine Learning Engineer

About The Company

Vanguard is a globally recognized leader in the investment management industry, committed to delivering long-term financial wellbeing for clients through innovative products and services. With a rich history of pioneering investment strategies and a client-centric approach, Vanguard emphasizes integrity, excellence, and continuous improvement. The company fosters a culture of collaboration, learning, and growth, empowering its employees to make meaningful contributions that shape the future of investment management. Vanguard's mission-driven ethos inspires teams across various regions, including Malvern and Melbourne, to work towards transforming clients' lives and achieving sustainable financial success.

About The Role

We are seeking a highly skilled Machine Learning Engineer to join Vanguard's dynamic team supporting model development and operations within the research and insights division of Investment Management. This role offers an exciting opportunity to collaborate closely with quantitative researchers, data scientists, and investment professionals to engineer, deploy, and maintain production-grade machine learning models that influence strategic decision-making and business insights. The successful candidate will be responsible for managing the entire machine learning lifecycle, from designing scalable pipelines and feature engineering workflows to automating training, deployment, and monitoring processes. Expertise in cloud-native architectures, particularly AWS SageMaker, along with strong software engineering fundamentals, is essential for success in this role.

Qualifications

The ideal candidate will possess a minimum of eight years of relevant work experience, including at least three years dedicated to development roles in machine learning, data engineering, or software engineering. An undergraduate degree or equivalent combination of training and experience is required, with a graduate degree preferred. Candidates should demonstrate extensive experience building and deploying machine learning solutions in production environments, with proficiency in Python and modern data science libraries such as Pandas, NumPy, Scikit-Learn, PyTorch, or TensorFlow. Hands-on experience with AWS services, especially SageMaker, is critical. Knowledge of MLOps practices, including CI/CD pipelines, model versioning, experiment tracking, and automated retraining, is also essential. Strong understanding of the software development lifecycle, testing strategies, and production support will enable the candidate to effectively collaborate across teams and ensure the reliability of deployed models.

Responsibilities

• Design, develop, and maintain end-to-end machine learning pipelines, from research phases to production deployment, ensuring scalability and robustness.
• Engineer cloud-native workflows for training, inference, and retraining using AWS SageMaker, optimizing for performance and cost-efficiency.
• Create and sustain feature engineering, feature storage, and data preparation pipelines to support model development and operational needs.
• Automate model deployment, testing, validation, and release processes utilizing CI/CD practices to ensure continuous integration and delivery.
• Build and manage batch, real-time, and event-driven architectures to support diverse operational requirements.
• Implement comprehensive model monitoring systems to track performance, detect drift, assess data quality, and maintain operational health.
• Collaborate with quantitative researchers and data scientists to operationalize research models, ensuring seamless transition from development to production.
• Manage model versioning, lineage tracking, experiment management, and reproducibility to ensure transparency and compliance.
• Optimize models for performance, scalability, and reliability while managing cloud costs effectively.
• Establish engineering standards, testing frameworks, and governance controls to uphold quality and security in ML solutions.
• Support ongoing production operations, respond to incidents, and continuously improve deployed models to adapt to evolving data and business needs.

Benefits

Vanguard offers a comprehensive benefits package designed to support the health, wellbeing, and professional growth of its employees. Employees enjoy competitive compensation, health insurance plans, retirement savings options, and paid time off. The company promotes a flexible work environment through its hybrid working model, enabling staff to balance in-person collaboration with remote work. Vanguard invests in ongoing learning and development opportunities, including training programs, workshops, and access to industry resources. Additionally, employees benefit from a collaborative culture that values innovation, diversity, and inclusion, fostering a supportive environment where everyone can thrive and contribute meaningfully to the company's mission.

Equal Opportunity

Vanguard is committed to creating a diverse and inclusive workplace. We believe that a variety of perspectives and experiences enhances our ability to serve clients and innovate effectively. We are an equal opportunity employer and do not discriminate based on race, ethnicity, gender, age, sexual orientation, disability, religion, or any other protected characteristic. All qualified applicants will receive consideration for employment without regard to these factors. We encourage individuals from all backgrounds to apply and join our team in advancing our mission to support long-term financial wellbeing for our clients.