Computer Scientist – Machine Learning / Computer Vision (GenAI Research Lead)

At AI Verse, we are developing breakthrough technologies to train Deep Learning networks and create the next generation of vision-based applications. Our focus is on merging synthetic data generation with real-world data to enable the next leap in AI-driven visual understanding.SummaryWe are looking for an experienced and visionary Computer Scientist in Machine Learning and Computer Vision to lead research on Generative AI for photorealistic image synthesis.The ideal candidate has a solid research background, strong hands-on ML/CV engineering experience, and a proven ability to guide a small technical team. You will design and supervise experiments at the intersection of generative modeling, domain adaptation, and photorealism.ResponsibilitiesLead R&D efforts on Generative AI models for high-fidelity image generation and domain adaptation.Evaluate and integrate architectures such as GANs, diffusion models, and ControlNets for synthetic-to-real transfer.Supervise the development and training of computer vision models in collaboration with ML engineers.Conduct experiments on image realism, texture fidelity, and lighting consistency using both synthetic and real datasets.Develop and validate pipelines for large-scale image generation, quality control, and automated evaluation metrics (FID, CLIP score, etc.).Stay current with state-of-the-art research in GenAI, publish results internally or externally when relevant.Provide technical mentorship, ensure code quality, and maintain scientific rigor across the team.QualificationsPhD or Master’s degree in Computer Science, Machine Learning, or Computer Vision.Proven experience in deep generative models (GANs, diffusion, ControlNet, StyleGAN, DreamBooth, etc.).Strong foundation in computer visionStrong proficiency in Python, PyTorch, and modern ML frameworksExperience leading research or technical projects in AIFamiliarity with dataset curation, image post-processing, and evaluation metricsStrong communication, organization, and mentorship skills.Curious, analytical, detail-oriented, and capable of transforming research insights into production-ready tools.