Dhruv is a Silicon Design Engineer at AMD building scalable infrastructure systems, with a passion for creating technology that solves real-world problems.
Combining hardware intuition with software craftsmanship to build transformative systems that scale effortlessly, always seeking the perfect balance between technical precision and practical impact
Combining hardware intuition with software craftsmanship to build transformative systems that scale effortlessly, always seeking the perfect balance between technical precision and practical impact
Experience
Advanced Micro Devices
Silicon Design Engineer II
June 2022 to Present
At AMD, I focus on developing scalable infrastructure to improve the robustness of emulation-based verification for flagship x86 processors. I’ve developed automated systems in Python and Perl, leveraging RabbitMQ and Slurm Scheduler workflows, that have eliminated over 200 monthly engineering hours. Furthermore, I engineered tools to bridge gaps in regression infrastructure, effectively improving emulation-based debug rate to 98%.
I used Langchain and FastAPI to develop LLM tools to improve bug triaging and provide debug teams with co-pilot assistance. I've also debugged functional and infrastructural issues found by the testbench, working across RTL, Assembly, and SystemVerilog code, and have implemented 8 logging and verification modules to improve testbench robustness. My contributions were recognized with a Talent Spotlight Award in 2024.
Marvell Technology
Engineering Intern
June 2021 to August 2021
During my internship at Marvell Semiconductor, I acquired proficiency in SystemVerilog and UVM methodology, which I leveraged to resolve hardcoded nanosecond time delays into clock-based delays in pre-existing verification kits. I engineered a foundational testbench and verification kit for the UVM-based verification of elementary FIFO RTL components and extended its use across 21 FIFO types.
University of Massachusetts-Amherst
Bachelor of Science | Computer Engineering
August 2018 to May 2022
As an undergrad at UMass Amherst, I served multiple roles that helped me develop leadership and technical skills. As president of UMass' Graphic Design club for six semesters, I led a leadership team of six, oversaw the organizing of design contests and events, and helped register over 100 new members. I worked as a Teaching Assistant for ECE 371 (Security Engineering), where I created labs for students, assisted the professor with lectures, graded examinations, and held office hours. I also served as a Technical Assistant for the Nursing and Engineering Innovation Center, where I set up and managed the center's contacts database.
Projects
Vuetor
iOS Application
May 2023 - December 2023
I developed an AI-powered tutoring platform that enables users to scan physical text for customized genAI-based learning assistance. I designed and built the user interface using UIKit and SwiftUI frameworks, implemented real-time text detection with VisionKit, and constructed a Python-based Flask API with PostgreSQL database to handle user authentication and OpenAI API integration.
SafeX
Motorcycle Helmet System
August 2021 - May 2022
For my undergraduate capstone project, I worked as apart of an interdisciplinary engineering team to develop an intelligent helmet system for motorcyclist safety. As computer vision lead, I implemented computer vision algorithms using YOLOv5 and OpenCV that achieved 99% accuracy in blind spot detection. Incorporating BLE protocols for communication between cameras and the helmet module, I also designed and 3D-printed specialized helmet attachments with LED-based visual alerts using embedded C and SPI.
UDistance
iOS Application & Sensor System
December 2020
As a HackUMass project, I worked on UDistance a hardware-software system to help students social-distance effectively. I developed an iOS application using Swift to display real-time population density in campus buildings. This app sourced data from a Firebase RTDB, which was populated by a collection of embedded Arduino system with IR sensors to track foot traffic. I implemented geofencing functionality for individual buildings using the Radar.io API to provide in-app alerts for users entering overcrowded areas.