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AI Research Engineer, ML Researcher

đŸ”„ 9 hours ago

🌏 Anywhere in the World

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧠 AI Research Scientist

đŸ—ŁïžđŸ‡©đŸ‡Ș German Required

PyTorch

Where this job comes from

Posted on Typus's careers page, not through a job board

First seen by Remote Rocketship 9 hours ago

Still open on Typus's site. Checked 32 minutes ago.

Logo of Typus

Typus

2 - 10 employees

Founded 2024

đŸ€– Artificial Intelligence

☁ SaaS

đŸ€ B2B

Artificial Intelligence ‱ SaaS ‱ B2B

Typus is an AI-powered visualization platform for architecture and real-estate professionals. It transforms CAD files, sketches and text prompts into photorealistic renders, upscales images to large-format/4K and 8K, and can generate cinematic video flyovers. Offered as a subscription (rather than per-image pricing), Typus integrates with common AEC tools (Revit, Rhino, SketchUp, IFC, Blender, Navisworks, Civil3D, Tekla, etc. ) via Speckle and provides a studio with editing tools (material swapping, masking, geometry control), floor-plan generators, and API access. The platform emphasizes secure EU data hosting, claims no use of customer uploads for AI training, and targets architects, interior designers, developers, real-estate firms, students and enterprises seeking faster, lower-cost visualization workflows and marketing assets.

📋 Description

‱ Design and further develop the core AI architecture for generative image models, with a focus on architecture-specific use cases. ‱ Develop, train, and optimize generative deep learning models, particularly diffusion models, UNet architectures, transformer-based approaches, and latent diffusion. ‱ Implement and evaluate fine-tuning and adaptation methods such as LoRA, DreamBooth-like techniques, and domain-specific transfer learning. ‱ Research controllable generation mechanisms, including region-based control, material- and texture-based conditioning, and structure- and context-aware image synthesis. ‱ Build and maintain scalable training and experimentation pipelines, for example using PyTorch, GPU clusters, and automated experiments. ‱ Systematically validate developed models through benchmarking, ablation studies, and qualitative and quantitative evaluation methods. ‱ Analyze, prepare, and curate training and evaluation datasets in the architectural context. ‱ Document research findings and methodological decisions to ensure reproducibility and knowledge transfer. ‱ Work closely with the product, frontend, and backend teams to transfer research results into production-ready AI systems. ‱ Provide technical support for the application and execution of research and grant-funded projects, such as IGP, KMU-innovativ, and EU programs. ‱ Monitor and assess current research developments in generative AI and identify relevant innovation opportunities.

🎯 Requirements

‱ Completed Master’s or PhD degree in Computer Science, Mathematics, Data Science, Computational Design, or a comparable discipline. ‱ Demonstrated expertise in generative AI for images, particularly in training deep learning models. ‱ Proven practical and/or academic experience with diffusion models, transformers, UNet architectures, latent diffusion models, and LoRA. ‱ Excellent understanding of deep learning methods, model architectures, training strategies, and evaluation techniques. ‱ Experience with research and experimentation workflows, reproducibility, and systematic model evaluation. ‱ Strong analytical thinking and an interest in connecting scientific research with product-oriented applications. ‱ Independent, structured working style and strong intrinsic motivation to explore and mature new AI approaches.

đŸ–ïž Benefits

‱ Grant-funded fixed-term employment with potential for a long-term position. ‱ Direct influence on a forward-looking deep-tech research project. ‱ Equity: The allocation of company shares is negotiable and forms part of the compensation package. ‱ Opportunity to take on a CTO role. ‱ Competitive, negotiable compensation. ‱ Permanent employment contract.

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