Dhruva Bhat
Résumé
TIMELINE / 2024—PRESENT

Engineering experience, in sequence

A chronological view of platform engineering, production software, scientific machine learning, and the education connecting them.

  1. May — Jul 2026Current
    EXPERIENCE

    AI Platform Engineering Intern

    Oak Ridge National Laboratory

    Asynchronous AI orchestration, resilient FastAPI services, and Dockerized ML workflows.

    • Built a FastAPI orchestration system coordinating multi-agent reasoning, retrieval, and molecular-generation services.
    • Implemented asynchronous execution, retries, caching, fallbacks, job tracking, and API-based service integration.
    • Containerized workflows with Docker and automated environment configuration.
    PythonFastAPIAsync I/ORAGDocker
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  2. Feb 2026 — PresentCurrent
    EXPERIENCE

    Full-Stack and Algorithms Developer

    Blueprint at Berkeley

    Production software and geospatial routing for Amigos de Los Rios.

    • Built a deployed volunteer-management platform with authenticated workflows and operational admin interfaces.
    • Designed geospatial routing logic around time, location, resource, and prioritization constraints.
    Next.jsReactSupabasePostgreSQLAWS Lambda
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  3. Mar 2025 — PresentCurrent
    EXPERIENCE

    AI Safety Research Assistant

    Carnegie Mellon University

    Modular LLM experimentation, model orchestration, API integration, and automated analysis.

    • Developed modular infrastructure for orchestrating LLM experiments across model environments.
    • Integrated inference APIs, configurable experiments, automated benchmarking, and analysis workflows.
    PythonLLM APIsExperiment pipelinesAutomated evaluation
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  4. Aug 2025 — PresentCurrent
    EXPERIENCE

    Machine Learning / Bioinformatics Research Assistant

    University of California, San Francisco

    Large-scale biological data pipelines and machine-learning workflows.

    • Built data and machine-learning pipelines for large biological datasets.
    • Processed more than 125,000 patient-data rows using preprocessing, clustering, PCA, and batch correction.
    RPCAClusteringBatch correctionData pipelines
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  5. Jun — Dec 2024
    EXPERIENCE

    Researcher at Stony Brook University

    Garcia Center for Polymers at Engineering Interfaces

    PyTorch, Gaussian-process, and predictive modeling for computational materials research.

    • Developed PyTorch representation-learning and Gaussian Process models for materials-property prediction.
    • Combined computational modeling with rheological analysis across thin-film and hydrogel research.
    PyTorchGaussian processesManifold learningRheology
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  6. Expected May 2028Current
    EDUCATION

    B.S. Electrical Engineering and Computer Science + Bioengineering

    University of California, Berkeley

    A dual technical foundation spanning computing systems, machine learning, and biological engineering.

    • Studying Electrical Engineering and Computer Science alongside Bioengineering.
    EECSBioengineering