Senior Machine Learning Engineer
About The Position
At Vanguard, we don't just have a mission—we're on a mission. To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best. Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.
Requirements
- Minimum of eight years related work experience, with at least three years of development experience.
- Undergraduate degree or equivalent combination of training and experience. Graduate degree preferred.
- Experience in software engineering, machine learning engineering, data engineering, or a related technical discipline.
- Strong experience building and deploying machine learning solutions in production environments.
- Expertise in Python and modern data science libraries (Pandas, NumPy, Scikit-Learn, PyTorch, TensorFlow, or similar).
- Hands-on experience with AWS services, including SageMaker
- Experience building and maintaining machine learning pipelines, feature engineering workflows, and model deployment processes.
- Knowledge of MLOps practices, including CI/CD, model versioning, experiment tracking, monitoring, and automated retraining.
- Strong understanding of software development lifecycle practices, testing strategies, and production support.
- Ability to work effectively with researchers, data scientists, and business stakeholders to deliver business outcomes.
Nice To Haves
- Graduate degree preferred.
Responsibilities
- Design, build, and maintain end-to-end machine learning pipelines from research through production deployment.
- Engineer scalable training, inference, and retraining workflows using AWS SageMaker.
- Develop and maintain feature engineering, feature storage, and data preparation pipelines.
- Automate model deployment, testing, validation, and release processes using CI/CD practices.
- Build batch, real-time, and event-driven architectures.
- Implement model monitoring for performance, drift detection, data quality, and operational health.
- Partner with quantitative researchers and data scientists to productionalize research models.
- Manage model versioning, lineage tracking, experiment management, and reproducibility.
- Optimize model performance, scalability, reliability, and cloud cost efficiency.
- Establish engineering standards, testing frameworks, and governance controls for ML solutions.
- Support production operations, incident response, and continuous improvement of deployed models.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Associate degree
