Cesar Hiersemann
ML EngineerComputer Vision & Machine Learning Engineer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Computer Vision & Machine Learning Engineer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Computer Vision Engineer with 9+ years of experience leading computer vision and deep learning projects in startup environments, primarily focused on multi-camera systems in edge deployments. Proven track record of operating as a highly autonomous technical owner and cross-functional team lead, successfully bridging the gap between applied R&D, hardware selection and production deployment. Committed to doing work with positive social impact.
Lund University
Master of Science in Engineering, Engineering Physics/Applied Physics
January 1, 2011 – January 1, 2017
S:t Petri Läroverk
Natural Science Programme
January 1, 2006 – January 1, 2010
Ever.Ag - Dairy
Principal Computer Vision Engineer
July 1, 2022 – January 1, 2026
Ever.Ag - Dairy
Computer Vision Technical Lead
July 1, 2019 – July 1, 2022
Ever.Ag - Dairy
Data Scientist & Computer Vision Engineer
August 1, 2018 – July 1, 2019
UNLEASH - Innovation lab for SDGs
Global Talent
June 1, 2018 – June 1, 2018
Singapore
Mesopo
Computer Vision & Machine Learning Engineer
September 1, 2017 – August 1, 2018
Mesopo
Junior Computer Vision Engineer
July 1, 2016 – August 1, 2017
The Faculty of Engineering at Lund University, LTH
Mathematics Tutor & Examiner
August 1, 2012 – December 1, 2015
Lund, Sweden
Inflated Multinomial Matching for Anchor-Free Object Detection
January 1, 2017 – December 1, 2017
Master Theses in M.Sc.Eng. Engineering Physics, specialization Images and Graphics. Presents and explores a novel stochastic matching strategy to enable training of anchor-free object detection models based on convolutional neutral networks.
Distracted Driver Detection
March 1, 2016 – June 1, 2016
Course project in Applied Artificial Intelligence Exploration of transfer learning using Deep CNNs (VGG16) for detection of driver behavior using images from passenger seat. Goal was to detect unsafe behavior such as texting, talking on the phone, applying makeup etc.
Cultural Fit Analysis
The candidate has a strong background in applied AI/ML, specifically in computer vision, which aligns well with roles requiring practical deployment and innovation. The experience spans both startup environments (Mesopo, Cainthus) and larger acquired entities (Ever.Ag), indicating adaptability to different organizational cultures. The focus on real-world problems (agriculture, driver detection, retail analytics) suggests a pragmatic and impact-driven mindset. The Master's thesis and course projects demonstrate a solid academic foundation complementing industry experience. The UNLEASH experience shows an interest in global challenges and collaborative problem-solving.
Soft Skills & Operational Fit
The candidate's experience as a Technical Lead and Principal Engineer suggests strong leadership, problem-solving, and project management skills. The descriptions of designing and implementing complex systems indicate a proactive and results-oriented approach. The early employee role at Mesopo and Cainthus suggests adaptability and a willingness to take on broad responsibilities in a startup environment. The UNLEASH experience indicates an interest in broader impact and collaboration.