Amadeuszย Szymko
Research And Development Engineer at TIER IV
Tokyo, Japan
Summary
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Top SchoolAmadeusz Szymko is an R&D engineer specializing in AI-driven robotics and perception for autonomous systems, with eight years of experience across academia and industry. Based in Tokyo and currently at TIER IV, he has led software teams integrating ROS 2 stacks for outdoor autonomous cargo delivery and developed real-time perception modules for complex tasks like wire harness assembly and racing-scale autonomy. His background blends hands-on ML and computer vision work for autonomous driving with practical system integrationโdelivering prototypes under tight timelines and shipping demonstrators used in factory and racing environments. Notably, he has translated research into deployable solutions (including LIDAR-based localization and telemetry for autonomous racing) and mentors student-focused platforms that bridge simulation to real-world testing.
8 years of coding experience
5 years of employment as a software developer
Master of Engineering - MEng, Electrical and Computer Engineering, Master of Engineering - MEng, Electrical and Computer Engineering at Faculdade de Ciรชncias e Tecnologia da Universidade Nova de Lisboa - FCT NOVAMaster of Engineering - MEng, Automation Control and Robotics, Master of Engineering - MEng, Automation Control and Robotics at Poznan University of Technology
Github Skills (64)
camera-calibration10
autonomous-vehicles10
calibration10
self-driving-car10
driver10
computer-vision10
lidar10
sensor10
ros10
autonomous-driving10
spell9
robotics9
v4l29
docker-container9
documentation9
Programming languages (8)
Github contributions (5)
tier4/autoware-ml
Dec 2025 - Jul 2026
Autoware-ML is a machine learning framework for multiple tasks, supporting the full lifecycle - training, evaluation, optimization, and deployment of models with native runtime support in Autoware.
Contributions:16 reviews, 28 PRs, 23 pushes in 6 months
machine-learningautonomous-vehicleautowareself-drivingsensors
autowarefoundation/managed_transform_buffer
Dec 2024 - Jan 2026
Contributions:1 release, 1 review, 32 PRs in 1 year
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