Dayhoff Labs is a startup developing foundational AI models for biochemistry and synthetic biology. During my first year here, I've identified key technical needs and designed a comprehensive software infrastructure for reproducible research.- Developed a unified coding environment with custom Docker containers, standardizing workflows across projects and reducing setup time for new team members.- Implemented a robust data management system built on DVC, introducing version control and streamlining access to datasets for our research team.- Created data pipelines using Dagster, ensuring reproducibility and traceability of our scientific datasets.- Configured and optimized distributed computing on Google Cloud Platform (GCP), significantly increasing our capacity for large-scale data processing and bioinformatics calculations.- Established best practices for software engineering within our primarily academic team, bridging the gap between research and industry standards.- Contributed technical expertise to writing grant applications, leveraging my unique combination of bioengineering and software engineering skills.As an early team member, I've taken initiative in identifying technical bottlenecks, researching solutions, and implementing tools that form the foundation of our startup's technical infrastructure. My work has enabled our research scientists to focus more effectively on their research in AI-driven biochemistry and synthetic biology.

Very is a fully-distributed IoT engineering firm, partnering with clients to build systems for smart manufacturing, smart energy & utilities, consumer electronics, and connected wellness.I worked with the full stack of software engineering needed to deploy data pipelines to production. My core competency is the Python data science stack (numpy, pandas, scikit-learn) and cloud infrastructure (AWS, GCP, Terraform), but I’ve also built RESTful web applications in Django and used SQL to manage our databases. I live in the Linux CLI and Dockerized CI/CD pipelines, not in isolated Jupyter Notebooks. I practice Agile and test-driven development, setting the culture of small teams to understand clients' needs and deliver code.

As an independent contractor, I worked with Correlation One and the Colombian Ministry of Information and Communication Technology (MinTIC) to teach a 10-week training program in ML and Data Science, for 300 professionals chosen through a competitive application.>> I managed hundreds of students through classes and projects.>> I worked with the chief instructor (Natesh Pillai, professor of statistics at Harvard) to design and implement the final project in the curriculum.

Hinge is an early-stage protein engineering startup.>> Designed the company's first data processing pipeline in Python, replacing legacy methods handled in Excel.>> Created in-depth literature reviews in cancer immunotherapy, resulting in three internal white papers used by the CEO during funding negotiations.>> Wrote and managed four SBIR small-business grant applications, bringing in $125,000 of grant funding.

Open Philanthropy is a major private science funder.>> Wrote scientific reports for non-technical decision makers, helping steer the organization's funding priorities in biotechnology and geoengineering.>> Organized a major call for research proposals. Our team triaged 120+ proposals, recruited external reviewers, conducted due diligence, and finally directed more than $10 million in grant funding to four research projects in the life sciences.

Ed Boyden's group develops tools to map, control, and repair the brain. My PhD focused on molecular tools to manipulate genetic molecules and program them into artificial living systems. I designed and built a new class of programmable RNA-binding proteins and a new method for designing genetic circuits using Synthetic Minimal Cells. My work led to two patents and two co-first-author publications, in PNAS and Nature Chemistry.