Founding Engineer
SoulBio
I joined SoulBio early and worked across data platforms, scientific applications, and client projects. As the company grew, I took on more product and technical direction. That includes deciding which problems to work on, where AI belongs in a scientific workflow, and which ideas are worth building.
AI and technical direction
- Defined SoulBio’s Scientific AI Enablement offering from the ground up: the problems it should solve, the core capabilities it needed, how knowledge and workflows should fit together, and how the resulting systems should be evaluated.
- Designed a biotech context graph that connects scientific knowledge with internal decisions, documents, and organizational context, then built a working proof of concept for customer discussions.
Scientific platforms and client work
- Built and deployed drug targetability and selectivity models that reduced hypothesis-validation time by ~40% for oncology teams.
- Architected and built ingestion and computation workflows for bulk and single-cell RNA-seq on a ~1 TB PostgreSQL system, bringing runs down from close to a day to a few hours.
- Built RNA-seq pipelines, APIs, analysis and visualization tools, and CI/CD workflows used in day-to-day scientific work.
- Managed AWS infrastructure for deployed applications, including reliability, scaling, performance, and cost trade-offs.
- Wrote product requirements and technical specifications for internal platforms and new client projects.
Applied ML and research
- Co-developed an LLM-powered search system that lets researchers query a corpus of 250K+ GEO datasets in natural language.
- Built a bioinformatics analysis agent for RNA-seq that coordinates multi-step workflows, automates repetitive analysis, and leaves scientific interpretation and decisions with the researcher.
- Co-authored research on the limits of GPT-based cell-type annotation and contributed technical articles and whitepapers.