AI Research Engineer, ML Researcher

This is a fully remote position, open to applicants anywhere in the world.

📋 Description

• Design and enhance the fundamental AI architecture for generative image models, emphasizing architecture-specific applications.

• Develop, train, and optimize generative deep learning models, with a particular focus on diffusion models, UNet architectures, transformer-based techniques, and latent diffusion.

• Implement and assess fine-tuning and adaptation strategies such as LoRA, techniques similar to DreamBooth, and domain-specific transfer learning.

• Investigate controllable generation methods, including region-based control, conditioning based on materials and textures, and structure- and context-aware image synthesis.

• Construct and sustain scalable training and experimentation pipelines, utilizing tools such as PyTorch, GPU clusters, and automated testing.

• Rigorously validate developed models through benchmarking, ablation studies, and both qualitative and quantitative evaluation methods.

• Analyze, prepare, and curate training and evaluation datasets within the context of architecture.

• Document research outcomes and methodological choices to ensure reproducibility and facilitate knowledge transfer.

• Collaborate closely with product, frontend, and backend teams to translate research findings into production-ready AI systems.

• Provide technical assistance for the application and execution of research and grant-funded projects, including IGP, KMU-innovativ, and EU programs.

• Monitor and evaluate current research advancements in generative AI to identify relevant innovation opportunities.

⛳️ Requirements

• A completed Master’s or PhD degree in Computer Science, Mathematics, Data Science, Computational Design, or a related field.

• Demonstrated expertise in generative AI focused on images, particularly in training deep learning models.

• Proven practical and/or academic experience with diffusion models, transformers, UNet architectures, latent diffusion models, and LoRA.

• Strong understanding of deep learning methods, model architectures, training strategies, and evaluation techniques.

• Experience with research and experimentation workflows, ensuring reproducibility and systematic model evaluation.

• Excellent analytical skills and a passion for connecting scientific research with product-oriented applications.

• Independent and structured working style, coupled with a strong intrinsic motivation to explore and advance new AI methodologies.

🏝️ Benefits

• Grant-funded fixed-term employment with the potential for a long-term position.

• Direct impact on a cutting-edge deep-tech research project.

• Equity: The allocation of company shares is negotiable and is included in the compensation package.

• Opportunity to step into a CTO role.

• Competitive and negotiable salary.

• Permanent employment contract.