The opportunity
Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades.
What you'll do
Lead the architecture and end-to-end delivery of scalable systems for: deploying, monitoring, and managing ML models in production
Own the technical direction for key platform areas: including model serving, the feature store, and ML observability infrastructure — from design through long-term reliability
Drive cross-functional partnerships with ML practitioners, data engineers,: and applied AI teams to streamline workflows, reduce friction, and accelerate experimentation
Evolve and scale our feature store to support efficient, low-latency feature: retrieval across real-time and batch use cases
Define and implement robust observability standards for model performance,: data pipelines, and feature freshness across the ML platform
Manage and optimize cloud compute resources (CPU/GPU) on AWS to support: cost-effective, high-throughput training and inference at scale
What they're looking for
- Contribute to technical strategy and roadmap discussions, and help mentor: engineers on the team through design reviews and hands-on guidance
- + years of software engineering experience, with meaningful depth in ML: infrastructure, data engineering, or model operations
- Demonstrated ability to own and deliver complex platform systems end-to-end, from architecture to production
- Deep expertise in model serving, distributed systems, and production ML workflows at scale
