This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
At Schwab, you’re empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us challenge the status quo and transform the finance industry together. We succeed as One Schwab—collaborating with trust, integrity, and a shared commitment to doing the right thing for our clients and each other. We are looking for a Senior AI/ML Engineer (individual contributor) who will join our Global Data and Analytics team to build and evolve a large‑scale data intelligence platform on Google Cloud Platform (GCP). In this role, you will build and scale fraud detection and decisioning capabilities across real-time and batch data pipelines. This role is ideal for someone who can design production-grade ML systems end-to-end: from data ingestion and feature engineering to model deployment, monitoring, and business decision integration. You’ll work at the intersection of data architecture and AI, engineering resilient pipelines that enable advanced analytics and machine‑learning models at scale. This is a hands‑on, end‑to‑end engineering role where your work directly supports teams protecting clients and strengthening trust across the firm.
Job Responsibility:
Design and build ML pipelines that combine streaming events and batch processing
Develop, deploy, and optimize models for risk scoring in user login, transactions and payment journeys
Create robust feature engineering pipelines and reusable feature definitions for both online and offline use
Integrate model outputs into operational decision systems
Implement MLOps best practices in standardizing the entire machine learning lifecycle
Improve data quality and reliability through schema governance, deduplication, lineage, and data validation checks
Partner with risk analysts and product stakeholders to convert business rules and feedback into model improvements
Contribute to analytics, dashboards, and LLM-assisted tooling that helps teams understand fraud patterns and model behavior
Requirements:
8+ years of professional experience in software, data, or machine learning engineering
3-4+ years of hands-on AI/ML experience building and deploying ML models in production
Advanced Python and SQL skills with deep BigQuery experience in large-scale data processing
Proven experience with both real-time and batch pipeline design
Demonstrated ability to own data preparation and quality
Strong understanding of feature engineering
Experience with cloud ML and data platforms for model training/deployment, data warehouse and orchestration
Experience integrating ML models into low-latency production systems for real time insights and decisioning
Nice to have:
Master’s or advanced degree in computer science or a related field
Experience with event-driven architectures and streaming platforms such as Kafka or Pub/Sub
Experience applying GenAI/LLM capabilities to analytics workflows
Experience applying advanced analytical techniques such as graph analytics, anomaly detection, entity linking for pattern detection
Hands-on experience with GCP ML services
Experience working in regulated environments with audit, compliance, and model governance requirements
Prior experience in fraud, risk, or financial crime analytics
What we offer:
401(k) with company match and Employee stock purchase plan
Paid time for vacation, volunteering, and 28-day sabbatical after every 5 years of service for eligible positions