About the Role
As a Senior Machine Learning Engineer, you’ll join our growing ML team to solve complex problems at the intersection of financial crime and modern banking. You’ll build and scale infrastructure and models to detect fraudulent activity in real-time, protect our customers, and ensure the safety of the financial system. You’ll be responsible for the full lifecycle of ML solutions, from data exploration and feature engineering to model training, deployment, and monitoring in production. This is a high-impact role where your work directly contributes to the mission of building a bank for startups.
What you’ll do
- Design, build, and maintain production-grade machine learning systems to detect and prevent financial crime.
- Collaborate with product, engineering, and risk teams to identify opportunities for ML, define requirements, and integrate solutions into our core products.
- Develop and deploy real-time fraud detection models and risk engines, leveraging diverse datasets and advanced ML techniques.
- Conduct exploratory data analysis, feature engineering, and model selection to optimize performance and explainability.
- Build and improve our ML platform, tools, and infrastructure to enable rapid experimentation, deployment, and monitoring of models.
- Drive best practices for MLOps, including continuous integration/continuous deployment (CI/CD), version control, and model governance.
- Mentor junior engineers and contribute to a culture of technical excellence and continuous learning.
You should have
- 5+ years of experience in machine learning engineering, with a proven track record of building and deploying ML systems in production.
- Strong proficiency in Python and experience with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
- Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes).
- Solid understanding of MLOps principles and practices, including model monitoring, logging, and alerting.
- Experience with data processing and big data technologies (e.g., Spark, Flink) is a plus.
- Familiarity with financial crime detection, risk management, or security domains is a plus.
- Excellent communication and collaboration skills, with the ability to translate complex technical concepts to non-technical stakeholders.
- A Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field.
