Karthik
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Work

Where I've worked.

Research labs, startups, and big tech.

  1. Samsung Research America

    AI Research Intern

    May 2026 - Aug 2026

    Samsung Research America

    • Built an on-device signal router for always-on ambient AI on Bixby Edge AI, filtering device signals on-phone at 95% suppression and 90% intent-routing accuracy to reserve frontier models for events that need them.
    • Built the developer SDK for Samsung's cross-device agent orchestrator, first deployed on XR glasses, for registering custom tools and skills, and unified several prototypes into one orchestrator.
  2. Peraton Labs

    Machine Learning Engineering Intern

    Jun 2025 - May 2026

    Peraton Labs

    • Developed RL agent for IoT malware detection using PyTorch and graph neural networks, reducing exploration latency by 35% and increasing detection coverage by 25% across 500K+ daily device events.
    • Built large-scale data analytics ETL pipelines and heterogeneous graph architecture with autoencoders to model device communication patterns, accelerating policy convergence by 40%.
  3. Memories.ai

    Computer Vision Researcher

    Feb 2025 - Aug 2025

    Memories.ai

    • Architected video memory framework for AR applications processing 10K+ streams using Python, Flask, and PostgreSQL, achieving 60% throughput improvement through frame sampling optimization.
    • Published Python SDK on PyPI with 2K+ downloads, implementing async processing and REST API integration for video analysis workflows.
  4. IDEAS Lab

    Undergraduate Robotics Researcher

    Mar 2025 - Jun 2025

    IDEAS Lab

    • Built real-time SLAM pipeline in C++ and Python with sensor fusion and Kalman filtering, improving 3D reconstruction accuracy by 25% and reducing latency by 30%.
    • Implemented neural radiance fields for novel view synthesis, generating photorealistic scene reconstructions for autonomous navigation.
  5. AgRPA

    Data Engineering Intern

    Aug 2024 - Dec 2024

    AgRPA

    • Built end-to-end weed detection pipeline processing 200GB+ drone imagery with Python and cloud storage, implementing parallel ETL workflows and optimized data partitioning for 40% faster analysis.
    • Engineered segmentation models achieving 92% accuracy on 50K+ images through agile sprints, reducing herbicide usage by 60% and costs by $150K annually.