Sangyoon Back
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Sangyoon Back Email & Phone Number

Machine Learning/Computer Vision Engineer @ Directed Machines at Directed Machines
Location: Seattle, Washington, United States 7 work roles 2 schools
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✓ Verified Jun 2026 3 data sources Profile completeness 86%

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Current company
Role
Machine Learning/Computer Vision Engineer @ Directed Machines
Location
Seattle, Washington, United States
Company size

Who is Sangyoon Back? Overview

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Quick answer

Sangyoon Back is listed as Machine Learning/Computer Vision Engineer @ Directed Machines at Directed Machines, a company with 7 employees, based in Seattle, Washington, United States. AeroLeads shows a matched LinkedIn profile for Sangyoon Back.

Sangyoon Back previously worked as R&D Machine Learning/ Computer Vision Engineer at Directed Machines and Research Assistant at Macs Lab (University Of Washington). Sangyoon Back holds Master'S Degree, Mechanical Engineering, Data Science from University Of Washington.

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Directed Machines

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About Sangyoon Back

Sangyoon Back is a Machine Learning/Computer Vision Engineer @ Directed Machines at Directed Machines.

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Sangyoon Back's current company

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Directed Machines
Directed Machines
Machine Learning/Computer Vision Engineer @ Directed Machines
seattle, washington, united states
Employees
7
AeroLeads page
7 roles

Sangyoon Back work experience

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R&D Machine Learning/ Computer Vision Engineer

Current

Seattle, Washington, United States

  • Specialized in real-time computer vision for hotspot detection on solar panels using thermal camera technology and a first principles approach. Developed efficient detection algorithms on Raspberry Pi 4 using C.
  • Designed and implemented an obstacle avoidance algorithm to enable autonomous robots to detect and navigate around objects in real time, improving safety and operational efficiency.
  • … Show more
  • Spearheaded the development of image annotation processes, converting annotations to JSON format for training object detection models with TensorFlow and PyTorch, enhancing model training efficiency and accuracy.
  • Developed a comprehensive analysis toolkit for evaluating object detection accuracy, which includes generating confusion matrices and monitoring CPU and memory usage to optimize system performance.
  • Created and deployed a semi-supervised learning system for real-time model training; the system annotates detected objects, generates JSON files automatically, and refines learning processes continuously.
Feb 2024 - Present

Research Assistant

Current
Macs Lab (University Of Washington)

Seattle, Washington, United States

  • Segmentation of the part surface for imaging based on curvatures and accounting for camera hardware​
  • Automated pose planning and movement to each imaging waypoint
  • Optimization of image acquisition including both adaptive lighting and camera parameters
  • Data-driven sensing and reinspection to account for environmental disturbances
  • Unsupervised training to recognize “good” part with supervised training to classify defects
Jan 2023 - Present

Teaching Assistant

Seattle, Washington, United States

  • Trained +30 students to 3D CAD design their own microfluidics device by AutoCAD and running the lab session.
  • Optimized laser cutter up to 250-micron meter and coached students how to use the laser cutter with different materials and thickness.
  • Demonstrated how to assemble each microfluidics layers and device testing microfluidics mixer design with two inlets and one outlet.
  • Guided students during the office hours and grading their homework.
Sep 2022 - Mar 2023

Research Assistant

Folch Lab (University Of Washington)

Seattle, Washington, United States

  • Automated drug testing: A robotic platform for high-throughput, high-fidelity drug testing on human biopsies
  • Applied computer-vision to the robot-arm that accessed an object's coordinates and synchronized robot-arm coordinates and camera pixel coordinate system.
  • Accomplished detecting 10-micron meter objects by computer-vision algorithm and pick-up and place objects into 96 wall-plates by python coding.
  • Designed a competitive lab orbital shaker by PLA 3D printable parts with a… Show more
  • Designed a competitive lab orbital shaker by PLA 3D printable parts with a stepper motor that run by an IR-receiver with Arduino programming. Show less
Mar 2022 - Jan 2023

Engineering Intern

Youmesys

Huntington, NY

  • Enhanced software skills and develop a technical background in cloud data management storage
  • Learned and applied fundamentals of supply chain, operational management, LEAN, and Six Sigma
  • Acquired communication between the manufacturer and customers for the supply chain management
Jul 2020 - Mar 2021

Mechanical Engineer

Blacksburg, Virginia, United States

  • Designed 3 end-effector prototypes and participated in concept selection of design
  • Created 2 protypes and validated their performances against customer product requirements
  • Utilized SolidWorks to create a CAD model and 3D printed each of its components for finger skeletons and air compressors
  • Developed a finger with force sensors, air compressors and rotary encoders and used electrical wiring to attach it to an Arduino microcontroller
  • Conducted FEA testing on fingers… Show more
  • Conducted FEA testing on fingers to ensure structural integrity of skeleton design while air compressors are operating
Aug 2019 - May 2020

Research Design Engineer

Mars Madness At Virginia Tech

Blacksburg, Virginia, United States

  • Participated in the Wheel and Suspension design team for a student-led organization that competes in NASA’s Human Exploration Rover Challenge
  • Researched optimal wheel dimensions, proper positioning of spokes, and rigid materials for assembly
  • Designed a 3D CAD model for rover wheels using SolidWorks and 3D printed them with ABS filament
  • Produced carbon fiber panels and used a lamination process to coat the ABS filament wheels for stiffness
May 2019 - May 2020
Team & coworkers

Colleagues at Directed Machines

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2 education records

Sangyoon Back education

FAQ

Frequently asked questions about Sangyoon Back

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What company does Sangyoon Back work for?

Sangyoon Back works for Directed Machines.

What is Sangyoon Back's role at Directed Machines?

Sangyoon Back is listed as Machine Learning/Computer Vision Engineer @ Directed Machines at Directed Machines.

Where is Sangyoon Back based?

Sangyoon Back is based in Seattle, Washington, United States while working with Directed Machines.

What companies has Sangyoon Back worked for?

Sangyoon Back has worked for Directed Machines, Macs Lab (University Of Washington), University Of Washington, Folch Lab (University Of Washington), and Youmesys.

Who are Sangyoon Back's colleagues at Directed Machines?

Sangyoon Back's colleagues at Directed Machines include Noa Dekel, Cole Martinson, Dennis Malone, Travis Johnson, and Chris Ransom.

How can I contact Sangyoon Back?

You can use AeroLeads to view verified contact signals for Sangyoon Back at Directed Machines, including work email, phone, and LinkedIn data when available.

What schools did Sangyoon Back attend?

Sangyoon Back holds Master'S Degree, Mechanical Engineering, Data Science from University Of Washington.

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