My Experience 💼

Hardware Development Engineer Intern 💻

Amazon Lab126

May 2023 - August 2023

For my Amazon Lab126 intern project, I took ownership of a $10,000 simulation tool for building 3D digital twins to simulate radar-based human presence detection in a home environment. I led weekly meetings with Engineers and Applied Scientists to dive further into the simulation tool. We identified 3 uses cases for in-home radar detection using the simulation tool. The use cases would be radar simulations of in-home environments with a single character, multiple characters and pets.

I learned how to use Unreal Engine and the Mixamo Library to create 3D home environments with objects and animated human characters for each radar simulation. I also created Python scripts to control the logic of each simulation and program a built-in, virtual radar sensor to collect data during each simulation and output a power delay profile. I took my project a step further by developing a python automation script that can generate 3D human animation videos using Unreal Engine. These videos are represented as synthetic data for an internal software pipeline that can generate radar signatures and train machine learning models for radar-based feature sets.


Click here to learn more about Amazon Lab126.

Hardware Development Engineer Intern 🤖

Amazon

May 2022 - August 2022

For my Amazon intern project, I developed a 5-stage workflow using a data science tool called KNIME that imports field metric data on operating temperature data of the Amazon Scout Robot and characterizes the robot's temperature usage model. The workflow will help improve waterfall planning and the fidelity of reliability tests. I worked with Reliability, Embedded, Hardware and Thermal Engineers to determine 4 key sub-systems to collect temperature data on for the workflow. These sub-systems were the MCU, battery, motors and sensors.

To collect data for the workflow, I wrote queries in SQL to collect 10,000+ random samples of robot temperature data from AWS Athena. Lastly, I developed Python scripts to build 5 data distributions (normal, weibull, bimodal, CDF, PDF) within the workflow based on the robot temperature data and combined those distributions along with summary statistics into a dashboard in KNIME. This dashboard represents the robot's temperature usage model.


Click here to learn more about Amazon Scout.

Undergraduate Technical Intern ⚙️

Intel Corporation

August 2021 - December 2021

For my first project during my Intel internship, I helped define 5+ initial stages of an Intellectual Property (IP) Model Workflow needed to achieve a standalone VCLP run on Intel IPs. With the guidance of SoC Engineers on my team, I was able to debug log files and errors from an Intel IP using Unix and used the VCLP tool (used for checking design quality) to run static checks on the IP we were working with.


In addition, I wrote up a Best-Known Method (BKM) that has instructions for setting up a front-end design environment and running VCLP tool flow to generate a VCLP run on a partition. This document will be referenced by Intel SoC Engineers for future development in their designs.

My second project during my Intel internship involved me learning the Jest testing framework to develop test suites containing 80+ unit tests along with 200+ test cases for utility functions that support Intel’s IP Summary Dashboard. This dashboard is a web-based application with information (bugs, features, and more) on SoCs/IPs from 15+ IPO projects. I also wrote function modules in JavaScript to improve the web infrastructure for Intel’s IPO dashboard that can track/file 1,000+ SoC tickets during project execution.


Click here to learn more about the VCLP tool, and here for information on Intel's Intellectual Property (IP) Cores.

Software Engineer Intern 👨🏿‍💻

Qualcomm

May 2021 - August 2021

For my Qualcomm intern project, I developed a Python tool that uses decrypted memory trace to obtain the run time memory footprint of an audio use case. I learned how to use Qualcomm Hexagon (QDSP6) development tools and a Hexagon simulation framework to decrypt memory trace generated from test cases of audio use cases. Examples of these use cases incliude calls, music, voice commands and video recordings. I was able to optimize the tool to parse 150M+ lines of memory trace based on user arguments, reducing the time it takes to process a memory footprint by 30%. My tool was added to productivity tools used by Audio DSP Engineers.