Job Description

:
This role is for one of Weekday's clients
Min Experience: 1 year
JobType: full-time
Requirements:
We're looking for a Computer Vision Engineer to help us take our AI-powered visualisation tool to the next level. The ideal candidate should have experience with deep learning, computer vision, and machine learning techniques. You'll work on enhancing visual accuracy and realism using models like Stable Diffusion and related generative AI technologies. Join us in refining a product that's reshaping the future of furniture-tech.
Key Responsibilities

• Design, develop, and optimize by fine-tuning diffusion models tailored for furniture detection, replacement, placement in images, and ultra-realistic rendering.
• Create ultra-realistic image re-rendering pipelines for seamless integration of furniture in diverse environments.
• Collaborate with product and design teams to understand requirements and deliver impactful AI-powered solutions.
• Research and implement state-of-the-art generative AI techniques for photorealistic image synthesis.
• Train, fine-tune, and deploy models, ensuring high accuracy and performance on large-scale datasets.
• Develop scalable APIs and systems to integrate model capabilities into production workflows by working closely with the DevOps Engineer.

Requirements
Experience:

• 3-8 years of experience in Generative AI with a focus on diffusion models.
• Solid experience building image generation models using architectures like Variational Autoencoders (VAEs), U-Net and Vision Transformers.
• Good understanding of classical computer vision techniques, and ability to solve 2D vision problems using OpenCV and Open3D.
• Practical experience working with 3D computer vision, including handling point clouds, 3D reconstruction, and processing 3D point-cloud data.
• Demonstrable experience in furniture or object replacement and re-rendering using generative models. Skills: Strong proficiency in frameworks like PyTorch
• Expertise in fine-tuning diffusion models (e.g., Stable Diffusion, Denoising Diffusion Probabilistic Models).
• Expertise in distributed training using CUDA-enabled GPUs.
• Solid knowledge of GANs, NeRF, or other generative models for realistic image synthesis.
• Familiarity with 3D rendering pipelines, textures, and lighting models.
• Proficiency in creating and working with large image-based datasets and optimizing model performance.

Soft Skills:

• Strong analytical and problem-solving skills.
• Excellent communication and ability to explain complex ideas and model architectures clearly.
• A proactive and collaborative mindset for teamwork.

Skills Required