Machine Learning Engineer

Our client is a technology company developing advanced AI and video intelligence solutions for real-world applications. They are looking for a Junior Machine Learning Engineer to join their Model Engineering team in Singapore.This is a hands-on opportunity for someone who wants to work deeply with machine learning models rather than simply using pre-built models. You will be involved in the development and improvement of computer vision models across areas such as:Training, fine-tuning and evaluating computer vision modelsWorking on tasks including image classification, object detection, segmentation, pose estimation and action recognitionExperimenting with different model architectures and training strategiesBenchmarking new approaches and analysing model performanceInvestigating model limitations and improving accuracy, robustness and efficiencyWorking with engineering teams on dataset quality and model performanceConsidering practical factors such as latency, throughput and computational efficiencyThe role provides exposure to the full computer vision model development lifecycle, from experimentation and training through to performance optimisation.Ideal Candidate:Open to fresh graduates or candidates with up to 2 years of experience, depending on relevant hands-on project experienceHands-on experience training, fine-tuning and evaluating computer vision modelsExperience with at least one of the following: classification, object detection, segmentation, pose estimation or action recognitionHands-on experience with PyTorch and/or TensorFlowStrong interest in Computer Vision / Deep Learning and model developmentBachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science or a related technical fieldPython experience is preferredDear Applicant,If you or someone you know is interested, please send the CV directly to [email protected] (most preferred, as I may overlook some CVs due to the high volume).Please note that visa sponsorship is not available at this time.