I build backend systems, quantitative models, and AI-enabled products. My work sits at the intersection of software architecture and mathematical modeling. I am a Caltech graduate based in Miami Beach, and I work remotely with teams that need senior engineering judgment to prototype quickly and turn promising ideas into production systems.
Before starting Miami Beach Labs, I spent 15+ years building software across startups and larger tech organizations, with a focus on simulation systems, machine learning infrastructure, data pipelines, and financial modeling.
At Capital One (Master Software Engineer) I helped design and build a simulation platform for synthetic financial data used in modeling and experimentation. The system evolved from early Python prototypes into a Haskell-based architecture built for reliability, composability, and large-scale simulation workflows. That work contributed to five patents related to agent-based modeling and synthetic data.
Earlier in my career I worked on machine learning systems, data science platforms, and large-scale backend services, including fraud detection, recommendation systems, NLP tools, and analytics infrastructure, with roles such as Machine Learning Engineer at Gumroad and Software Architect at CollabRx. I also worked as a Haskell quantitative engineer at Tontine Trust building simulation and forecasting systems.
Areas of work
- Backend architecture and system design
- LLM integration into existing production workflows
- Rapid prototyping that evolves into production systems
- Quantitative modeling and simulation for decision making
My technical interests include functional programming, machine learning systems, simulation, quantitative finance, and stochastic models of complex systems. Outside of client work I explore formal reasoning about software and build small experiments that clarify ideas. Beyond Caltech, my graduate studies in intelligent systems and applied mathematics deepened my background in probabilistic modeling and AI.