Braden Hechmer

Product Operations

Nashville, TN · ebhechmer@gmail.com

Hello! My name is Braden, and I'm passionate about machine learning, algorithms, and innovative problem-solving. I thrive on building impactful solutions and exploring the intersection between technology and real-world challenges.


Currently, I’m working at a startup focused on revolutionizing the recruiting process in the manufacturing industry. I’m working on the product operations of our AI-powered platform that helps find, screen, and vet skilled workers for high-paying manufacturing jobs within our initial market.

Experience

Product Operations

Laborup
  • Empowering America’s industrial base.

    We are proud to serve the critical industries and manufacturing workers that keep the world moving, strengthen our economy, and protect our democracy. From engineering and machining to welding, fabrication, maintenance, quality control, and mechatronics, these are the experts driving innovation and resilience in American manufacturing.

    We’re honored to be backed by mission-driven, world-class investors who share our vision of empowering workers, strengthening industries, and revitalizing America’s industrial base.
June 2025 - Present

Engineer / Product Manager, Intern

Laborup
  • Launched our initial product. Found market adoption.
  • Achieved a 1000% increase in user acquisition within just 2 months of targeted growth strategies. Established a foundation for scalable growth.
  • Closed initial contracts with mid-market manufacturers. Proved early market validation.
  • Secured significant pre-seed funding. Gathered investments from top Silicon Valley investors that are on the boards of LinkedIn, Airbnb, Instacart, Pinterest, and more.
December 2023 - May 2025

Full-Stack Software Developer, Intern

Loch and Key Productions
  • Developed and launched comprehensive, user-friendly websites that effectively addressed unique client needs and specifications
  • Collaborated closely with the marketing and production teams to integrate visual designs, content, and functionality requirements seamlessly
  • Managed both front-end and back-end components, driving improved user experiences and efficient server operations
May 2023 - August 2023

Undergraduate Research Assistant

TENNLab | University of Tennessee, Knoxville
  • Developed and implemented neuromorphic algorithms using PyTorch and additional libraries, contributing to TENNLab's research in brain inspired computing
  • Trained spiking neural networks utilizing deep learning techniques, backpropagation algorithms, and neuroscience principles to optimize model performance
  • Actively working on implementing Neural Architecture Search (NAS) using snnTorch to automate the design of neural network architectures for various applications
December 2022 - August 2023

Education

University of Tennessee, Knoxville

Bachelor of Science
Computer Science | Minor in Machine Learning
August 2022 - May 2025

Projects

AI Pomodoro Timer

January 2024-May 2024
Skills Used: Javascript, React, OpenAI, PostgreSQL, RedwoodJS, Figma
  • Developed a full-stack Pomodoro timer application utilizing RedwoodJS, React, and PostgreSQL, enhancing productivity through AI-driven features and user-friendly design
  • Designed and implemented core functionalities including AI-assisted journaling, and a customizable timer
  • Collaborated in a two-person team to execute end-to-end development, from initial design in Figma to deployment on Netlify
  • Integrated OpenAI’s GPT API to provide personalized feedback in the journaling feature, improving user experience and setting the groundwork for future machine learning enhancements
Learn more about the project:

Iearn

Stanford Hackathon 2024
Skills Used: Python, Flask, OpenAI, Javascript, Supabase, Pinecone, Expo
  • With a team of 3 others, we created a comprehensive mobile app that solves the issue of effectively delivering educational content. Iearn provides addictive, byte-sized learning with a Pinterest-like multimedia card, Reddit/Quora like lifestyle, Duolingo-like gamification, and a TikTok-like feed. All of this was implemented with a way to provide compensation to users who ask valuable questions.
  • Utilized OpenAI's GPT model to create a series of answer modules in JSON format from the users questions. OpenAI was also used to create a full sized card that would pop up on a users "for you page".
  • Created a full-fledged reccomendation system, using Pinecone, to provide users with the best content relative to their interests. User behavior was tracked using tags.
  • RAG (Retrieval Augmented Generation) was used to create cards with trustworthy, human-generated content to go alongside the AI generated content created from user questions. This introduces subjectiveness, human nature, and opinions into the content which was not achieveable via usage of LLMs alone.
Learn more about the project:

ML-ody

MIT Hackathon 2022
Skills Used: Python, OpenCV, Numpy
  • Created a new way to learn piano as a beginner. The program uses OpenCV to read a piece of sheet music and then convert it into step by step instructions for the user to follow, including which keys to press and when to press them.
  • Used OpenCV to parse through the sheet music uploaded and cross reference that image to detect symbols such as staff lines, sharps, flats, and different types of notes.
  • Used numpy to create a 2D array of the sheet music, and then used that array to create individual images of each note and symbol on a piano. This images were concatenated into a single image, and then displayed to the user on the site.
Learn more about the project:

Skills

Primary Programming Languages
  • Python
  • C++
  • Javascript

    • Machine Learning
  • PyTorch
  • Tensorflow
  • Scikit-Learn
  • Keras
  • Numpy
  • Pandas
  • Matplotlib

    • Databases
  • PostgreSQL
  • MongoDB
  • MySQL

    • Tools/Libraries
  • Git
  • Docker
  • React
  • TailwindCSS

    • Awards & Certifications

    • Cum Laude Graduate and Dean's List (2022-25)
    • Boyd Venture Challenge Winner of $15,000 (2024) · Read More

    • Machine Learning Specialization
    • Unsupervised Learning, Recommenders, Reinforcement Learning
    • Advanced Learning Algorithms
    • Supervised Machine Learning: Regression and Classification