Henrik Söderlund
Product Owner at Zenseact
Gothenburg, Västra Götaland County, Sweden
Summary
👤
Senior🎓
Top SchoolHenrik Söderlund is a Product Owner with eight years of hands-on experience in autonomous systems, currently shaping lidar and AD product delivery at Zenseact. Trained as an electronics engineer with an M.Sc. in Robotics and Control, he blends sensor fusion, control theory and AI expertise from roles at Volvo Cars and Zenseact into pragmatic product decisions. He has moved between engineering and delivery roles—radar and lidar integration, scrum master duties, and product ownership—giving him a rare view across implementation, validation and team execution. Henrik contributes to open-source multi-object tracking tooling, improving cross-platform validation and dataset handling, reflecting a focus on robust evaluation pipelines. A natural problem-solver and team player, he enjoys reframing complex autonomy challenges into testable, deployable solutions. Based in Gothenburg, he is motivated by making self-driving vehicles reliable and accessible for real people, not just technology for its own sake.
8 years of coding experience
6 years of employment as a software developer
Master's degree Electronics with specialization in Robotics and Control, Master's degree Electronics with specialization in Robotics and Control at Umeå University
Swedish, English, Spanish
Github Skills (7)
multi-object-tracking10
machine-learning10
python10
deeplearning-ai9
deep-learning9
opencv6
git4
Programming languages (8)
Github contributions (5)
mikel-brostrom/boxmot
Dec 2022 - Dec 2022
BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
Role in this project:
ML Engineer
ML EngineerContributions:4 reviews, 6 commits, 12 PRs in 3 days
Contributions summary:Henrik primarily contributed to the validation and evaluation aspects of the project, focusing on making the validation script compatible with Windows environments. They also made improvements to the dataset download process, including adding a flag file to manage storage and download. The user's commits demonstrate a focus on maintaining and refining the codebase for multi-object tracking, particularly in handling dependencies and environment configurations.
tracking-by-detectionosnetdeep-learningobject-detectioncomputer-vision
henriksod/Yolov5_StrongSORT_OSNet
Aug 2022 - Feb 2023
Real-time multi-camera multi-object tracker using YOLOv5 and StrongSORT with OSNet
Contributions:54 commits, 74 pushes, 8 branches in 6 months
object-trackerosnettrackercameraobject-detection
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