Click here for a PDF version of my resumé.

Education

  • B.S. in Biology @ Massachusetts Institute of Technology (MIT)
    • Attended: August 2020 - February 2025
    • Cumulative GPA: 4.6 / 5.0
    • Coursework Highlights:
      • Biology: Cell Biology (7.06), Molecular Basis of Infectious Disease (7.26), Molecular Biology (7.28), Microbial Physiology (7.21), Applied Molecular Biology Laboratory (7.003), Topics in Computational and Systems Biology (CSB.100)
      • Computer Science: Mathematics for Computer Science (6.1200), Fundamentals of Programming (6.1010)
      • Chemistry: Organic Chemistry I (5.12), Fundamentals of Spectroscopy (5.351), Synthesis of Coordination Compounds and Kinetics (5.352), Macromolecular Prodrugs (5.353), Introduction to Biological Chemistry (5.07), Thermodynamics I (5.601) and II (5.602)
      • Math/Physics: Single-variable Calculus (18.01), Multi-variable Calculus (18.02), Mechanics (8.01), Electricity & Magnetism (8.02)

Software Engineering Experience

  • Software Engineer (L3) @ Google | May 2025 - Present (2+ months)

    • Active contributor to kubernetes-sigs/jobset, a Kubernetes-native API for deploying HPC and AI/ML workloads at scale
    • Integrate and meticulously test new GPU/TPU accelerator models for Google Kubernetes Engine (GKE)
    • Work as part of GKE AI Infrastructure team provide direct support to customers running AI/ML workloads

  • Software Engineering Intern @ Google | May 2023 - August 2023, June 2024 - August 2024 (6 months)

    • Implemented horizontal scaling for the Kubernetes control plane proxy (kubernetes-sigs/jobset) in Golang, demonstrating ability to troubleshoot and improve complex data-intensive systems
    • Extended internal cluster developer tooling, deploying changes to world’s largest fleet of Kubernetes clusters

  • Software Engineering Co-Op @ Wayfair | Jan 2022 - Aug 2022 (8 months)

    • Implemented and deployed microservice APIs for data pipelines using Java, Python, PostgreSQL, and Kubernetes

Research Experience

  • Research Intern @ MIT (Sinskey Lab) | Feb 2023 - May 2023, Sep 2024 - Dec 2024 (8 months)

    • Conducted molecular (ddPCR), biochemical (Western blot, nanoparticle tracking analysis), and cell-based (ACE2⁺ cell transduction) assays to verify proper assembly of SARS-CoV2 structural proteins into virus-like particles (VLPs)
    • Quantified viral DNA, RNA, and capsid protein levels from a bioreactor-based adeno-associated virus (AAV) production platform using nanodrop, qPCR, and ELISA
    • Performed routine laboratory tasks while following best practices: sterile buffer/media preparation, aseptic mammalian cell culture (HEK-293 cell line), data collection, and sample management

  • Research Intern @ MIT (Jensen Lab) | Sep 2022 - Dec 2022 (4 months)

    • Expanded a method of chemo-enzymatic retrosynthesis in Python to enable greener synthesis pathways
    • Presented poster @ the 2022 MIT Machine Learning for Pharmaceutical Discovery and Synthesis Consortium

  • Early College Intern @ D. E. Shaw Research | May 2021 - Aug 2021 (4 months)

    • Optimized accuracy of free energy perturbation (FEP) molecular dynamics simulations of ligand:receptor systems
    • Created novel method of FEP network generation in Python using integer linear programming

  • Staff Research Associate @ UC San Diego (Kufareva Lab) | Oct 2019 - Dec 2020 (15 months)

    • Trained in mammalian tissue culture in a BSL-2 biosafety cabinet, performing cell viability assays and routine passaging of HEK-293 cells for protein expression studies
    • Wrote toolkit to verify, validate, and visualize Boolean models of cell signaling networks in R and Python
    • Explored literature on immune cell signaling pathways to create computational model and perturbation-response database of CCR2⁺ monocyte chemotaxis
    • Analyzed proteome-wide phosphorylation responses of CCR2⁺ immune cells in response to CCL2 and inhibitor analogue treatment through linear modeling on mass spectrometry data

Publications

  1. Carretero Chavez, W.; Krantz, M.; Klipp, E.; Kufareva, I. kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models. BMC Bioinformatics 24, 246 (2023). doi:10.1186/s12859-023-05329-6.