Machine Learning Engineer – Computer Vision

This is a fully remote position, open to applicants in Poland.

📋 Description

• Design, construct, and implement scalable computer vision systems utilizing deep learning techniques.

• Create models for classification, detection, segmentation, and visual quality evaluation.

• Partner with data scientists and engineers to enhance data pipelines and training datasets.

• Optimize models for performance across both cloud and edge platforms.

• Stay updated on computer vision research and integrate applicable advancements.

• Lead the development and deployment of vision-based models for object detection, tracking, segmentation, and image/video comprehension.

⛳️ Requirements

• Over 5 years of experience in Machine Learning, with a minimum of 3 years dedicated to computer vision.

• A proven history of delivering vision-based machine learning products or conducting research.

• Expertise in deep learning frameworks such as PyTorch or TensorFlow.

• Strong proficiency in Python programming.

• Familiarity with OpenCV and various image processing libraries.

• Solid understanding of convolutional neural networks (CNNs), transformers, and associated architectures.

• Experience with model optimization and deployment on cloud or edge devices.

• Experience with video analytics, multi-modal models, or synthetic data generation is a plus.

• Publications or contributions to open-source projects in the field of computer vision are advantageous.

• Familiarity with MLOps tools like MLflow, DVC, and Docker is a plus.

🏝️ Benefits

• Competitive salary and comprehensive benefits package.

• Opportunities for career growth within an expanding tech company.

• A culture of continuous learning: mentorship, internal training, and certification programs.

• A flexible and agile work environment (remote, hybrid, or on-site in Kraków).

• Office perks including excellent coffee, tea, fresh fruit, snacks, and a lively atmosphere.

• A flat management structure that values your input.

• Regular team activities and a supportive, social work culture.

• Emphasis on work-life balance, remote work options, and asynchronous collaboration.

• An inclusive culture that prioritizes diversity, equity, and inclusion.