About

Trajectory & background

Software Engineer at Google

Ph.D. from Stanford University experienced in AI/ML, large language model (LLM) evaluation, high-performance computing (HPC), and large-scale data systems. Interests include agentic AI frameworks, LLMs, reinforcement learning, graph machine learning, and probabilistic risk modeling, with a focus on bridging cutting-edge AI with real-world engineering and data challenges. Recent work includes a NeurIPS 2025 paper and oral presentation on LLM robustness under paraphrase stress tests.

Career History

Experience

Professional, academic research, and teaching appointments.

  1. Software Engineer

    Google

    Software engineering at Google.

  2. AI Research Engineer

    Daice Labs · Boston, U.S.

    Designing hybrid AI systems that integrate foundation models with neurosymbolic reasoning and biologically inspired computational principles. Developing robustness evaluation and governance tooling to audit system behavior under contextual variation, and collaborating across engineering and product teams to translate research prototypes into scalable, production-ready AI solutions.

    • Prototyping hybrid composite AI architectures that combine foundation models with neurosymbolic components and bio-inspired system design for adaptive, auditable workflows.
    • Developing evaluation and governance-minded tooling to stress-test hybrid systems under changing context and to support traceable, explainable decision-making.
    • Collaborating across engineering and product teams to translate research findings into production-ready AI solutions.
  3. Graduate Researcher

    Stanford University · Stanford, U.S.

    • Developed and published a reproducible evaluation protocol (NeurIPS 2025 Workshop) measuring surface-form robustness of LLMs via paraphrase stress tests, using Mistral-7B and Qwen2.5-7B with deterministic 4-bit inference on NVIDIA A100.
    • Designed and implemented large-scale HPC pipelines for nonlinear dynamic simulations of reinforced concrete buildings, scaling to hundreds of thousands of earthquake simulations on Stanford’s Sherlock cluster.
    • Built a modular Python data ecosystem integrating DuckDB (relational metadata) and HDF5 (time-series arrays), enabling fast querying and analysis of multi-terabyte structural simulation datasets.
    • Parametric calibration of the Reinforcement Ductile Fracture Model (RDFM), integrating mechanics and ML to predict reinforcement fracture under low-cycle fatigue of reinforced concrete shear walls; validated with 23 laboratory experiments.
    • Applied machine learning methods (Bayesian inference, probabilistic risk modeling) to quantify collapse fragility, resilience metrics, and post-earthquake degradation of building systems.
    • Produced visualization and analytics tools (Matplotlib, Jupyter, HPC logging) for real-time monitoring and reproducibility of simulations across thousands of jobs.
  4. Teaching Assistant, CEE 280 Advanced Structural Analysis

    Stanford University · Stanford, U.S.

    • Mentored students in object-oriented software design for structural analysis engines, guiding projects from architecture to implementation.
    • Led tutorials on structural analysis methods and programming skills, increasing student performance and engagement.
    • Provided detailed feedback on code and analysis workflows, reinforcing best practices in version control, modularity, and reproducibility.
  5. Structural Engineer

    Balderrama & Silos Ingeniería Estructural, S.C. · Guadalajara, Mexico

    • Co-led structural design of a $500M industrial facility and multiple commercial/residential projects, delivering structural systems from analysis through construction.
    • Developed custom algorithms and automation tools for analysis/design of structural elements, improving team efficiency and reducing manual calculation time.
    • Directed a team on the design of a 5-story residential building in Mexico City and the data center building for Megacable in Guadalajara, Mexico.
    • Managed structural project workflows, coordinating engineers to ensure delivery under tight deadlines.

Degrees & Honors

Education

Academic credentials and graduate research training.

  1. Stanford University

    Ph.D. in Structural Engineering; doctoral training in Computer Science

  2. Stanford University

    M.S. in Structural Engineering

  3. Universidad de Guadalajara

    B.S. in Civil and Environmental Engineering; GPA: 97.38/100; Summa Cum Laude

  4. Institut National des Sciences Appliquées de Lyon (INSA de Lyon)

    Study abroad program in Civil Engineering and Urban Planning

Technical Foundation

Skills & languages

Core technical competencies, tools, and spoken languages.

  1. AI & Machine Learning

    • PyTorch
    • TensorFlow
    • Scikit-learn
    • Graph ML
    • Bayesian Inference
    • Probabilistic Modeling
    • LLMs
    • Agentic AI
    • Retrieval-Augmented Generation (RAG)
    • Vector databases
  2. Agentic & Orchestration Frameworks

    • ADK
    • LangChain
    • LangGraph-style workflows
    • Multi-step reasoning pipelines
    • Tool-augmented LLM systems
  3. Data Engineering & Databases

    • SQL
    • AWS (S3, EC2)
    • HPC job scheduling (SLURM)
    • DuckDB
    • HDF5
    • Data pipeline design
  4. Software Development

    • Object-Oriented Programming (OOP)
    • Git
    • Docker
    • REST APIs
    • CI/CD
    • FastAPI
  5. High-Performance Computing

    • Parallel processing (multiprocessing, MPI concepts)
    • Cluster workflows
    • Scalable data handling (multi-TB datasets)
  6. Programming

    • Python
    • SQL
    • C++
    • LaTeX
    • MATLAB
    • TCL
    • Bash
  7. Languages

    • English (Bilingual)
    • Spanish (Native)
    • French (Full professional, DELF B2)

Graduate Training

Relevant coursework

Selected graduate and technical coursework.

  1. Stanford University

    • CS221Artificial Intelligence: Principles and Techniques
    • CS230Deep Learning
    • CS229Machine Learning
    • CS224WMachine Learning with Graphs
    • CS205LContinuous Mathematical Methods with an Emphasis on Machine Learning
    • CS231NDeep Learning for Computer Vision
  2. AWS

    • Generative AI with LLMs

Fellowships & Awards

Honors & awards

Fellowships, research distinctions, and academic honors.

  1. Top Reviewer

    Conference on Neural Information Processing Systems (NeurIPS).

  2. Best Reviewer Award

    NeurIPS 2025 Workshop on Evaluating the Evolving LLM Lifecycle.

  3. John A. Blume Fellowship

    School of Engineering, Stanford University.

  4. Gere Award

    School of Engineering, Stanford University.

  5. The Shah Family Fund Fellowship

    School of Engineering, Stanford University.

  6. Nancy Grant Chamberlain Fellowship

    School of Engineering, Civil and Environmental Engineering, Stanford University.

  7. Dionisio Garza-Medina Fellowship

    School of Engineering, Stanford University.

  8. Nancy Grant Chamberlain Fellowship

    School of Engineering, Civil and Environmental Engineering, Stanford University.

  9. Claudio X. González Fellowship

    School of Engineering, Stanford University.

  10. Fulbright-García Robles Scholarship

    U.S.-Mexico Commission for Educational & Cultural Exchange (COMEXUS).

  11. Distinguished Academic Performance Award

    Ceremonia de Reconocimiento a Estudiantes Sobresalientes, CRES 53rd Ed., Universidad de Guadalajara.

  12. Academic Excellence Award

    Colegio Metropolitano de Ingenieros Civiles de Jalisco, A.C., COMICIJ.

  13. México Francia Ingenieros Tecnología (MEXFITEC) Fellowship

    Secretaría de Educación Pública, SEP.

  14. Programa de Estímulos Económicos a Estudiantes Sobresalientes (PEEES)

    Universidad de Guadalajara. Scholarship for high academic performance.

  15. Excellence Scholarship

    Secretaría de Educación Pública, SEP. Stipend awarded to high academic performance students.

  16. TELMEX Scholarship

    TELMEX. Stipend awarded to high academic performance students.