Machine Learning Engineer

Skylum allows millions of photographers to make incredible images. Our award-winning software automates photo editing with the power of AI yet leaves all the creative control in the hands of the artist. Join us on our mission to make photo editing enjoyable, easy, and accessible to anyone. You’ll be developing products with innovative technologies, providing value and inspiration for customers, and getting inspired in return. Thanks to our incredible team of experts, we’ve built a collaborative space where you can constantly develop and grow in a supportive way. At the same time, we believe in the freedom to be creative. Our work schedule is flexible, and we trust you to give your best while we provide you with everything you need to make work hassle-free. Skylum is proud to be a Ukrainian company, and we stand with Ukraine not only with words but with actions. We regularly donate to various organizations to help speed up the Ukrainian victory. Role Mission: We are looking for a senior Machine Learning Engineer who can build and improve ML models used in our product. Most of the tasks are related to computer vision and image processing. You will work on both common tasks like segmentation, detection, and classification, as well as more complex problems involving Diffusion Models, LLMs, VLMs, and other custom tasks without ready-made solutions. You should be able to work independently, move fast, and quickly test new ideas. Responsibilities: Build and improve ML models, mostly for computer vision tasks. Handle the full ML process: collect and prepare data, train and test models, and improve them step by step. Work on both standard and new types of problems. Create fast prototypes and help turn successful ones into real products. Work closely with other teams to bring your models into the product. Requirements: 4+ years of real-world experience with ML, including at least 2 years in computer vision. Good understanding of how to build ML systems from start to finish. Hands-on experience with one or more of these: object detection, segmentation, img2img, generative networks. Ability to read, understand, and implement ideas from cutting-edge research papers. You stay current with top conferences (e.g., CVPR, NeurIPS, ICCV) and can turn academic innovations into practical solutions. Strong skills in Python and PyTorch. Experience optimizing models for efficient inference on local devices (CPU, GPU, NPU), including quantization, pruning, and runtime adaptation. Able to work with and explore large datasets. Comfortable working on your own and taking full responsibility for your tasks. Experience with prototyping and working in an R&D cycle. Nice to Have Experience in research (PhD, papers, or personal projects). Experience training or fine-tuning foundational models (LLMs, VLMs). Experience training or fine-tuning diffusion models for specific image generation or enhancement tasks. Familiarity with classical image processing techniques. Experience wi