Machine Learning Engineer – Computer Vision & Deep Learning

Overview

As a Machine Learning Engineer, you will design, train, and deploy deep-learning models for computer-vision tasks, focusing on CNNs and Vision Transformers to enable detection, tracking, and perception in real-world settings. You’ll bridge research and production, developing robust, efficient systems that run across cloud, desktop, and edge environments. You join a cross-functional team to translate cutting-edge CV research into reliable products for demanding environments. This role offers opportunities across junior to senior levels with a clear path for technical leadership.

Responsibilities

• Design and develop deep-learning models for computer vision tasks
• Build and optimise CNN and Vision Transformer architectures
• Develop models for object detection, tracking, segmentation, classification, pose estimation
• Design neural-network architectures, loss functions and training strategies
• Create data-processing, training, validation and evaluation pipelines
• Prepare and manage image and video datasets for model development
• Define evaluation metrics and analyse model performance
• Incorporate recent CV and DL research techniques
• Optimize models for accuracy, robustness, latency and efficiency
• Translate research prototypes into production-quality software
• Support deployment across cloud, desktop and edge environments
• Collaborate with ML Engineers, Software Engineers, Researchers, Tech Leaders and customers
• Contribute to architecture decisions, technical planning, testing, code reviews and CI/CD practices

Key requirements

• Practical experience developing deep-learning solutions for computer vision
• Strong understanding of Convolutional Neural Networks
• Experience with Transformer architectures for computer vision
• Experience using PyTorch
• Strong Python development skills
• Experience with object detection, tracking, segmentation, classification or pose estimation
• Experience training, validating and evaluating deep-learning models
• Understanding of neural-network architectures, loss functions, hyperparameter optimization and model-performance evaluation
• Experience with image, video or sensor datasets
• Interest in recent ML and CV research
• Good software-engineering practices (Git, testing, maintainable code)
• Strong analytical, problem-solving and communication skills

• analytical thinking
• problem-solving
• clear communication
• PyTorch
• TensorFlow (strongly considered)
• Python

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