Senior Computer Vision Engineer

This role is for one of the Weekday's clients

Min Experience: 3 years

JobType: full-time

Requirements

Key Responsibilities

• Design, develop, and fine-tune diffusion models for tasks such as furniture detection, placement, replacement, and ultra-realistic rendering in images.
• Build advanced image re-rendering pipelines that allow seamless integration of furniture into varied real-world environments.
• Collaborate with product and design teams to understand user needs and develop impactful AI-driven visual solutions.
• Research and implement cutting-edge generative AI techniques for photorealistic image synthesis.
• Train, optimize, and deploy large-scale models, ensuring high fidelity and accuracy across diverse datasets.
• Work closely with DevOps to develop scalable APIs and integrate AI models into production environments.

Requirements

Technical Experience:

• 38 years of hands-on experience in Generative AI, with a focus on diffusion models.
• Proven expertise in object or furniture replacement, placement, and realistic re-rendering using generative approaches.

Core Skills:

• Proficiency inPyTorchfor deep learning model development.
• In-depth knowledge and experience fine-tuning diffusion models (e.g., Stable Diffusion, DDPM).
• Experience withGANs,NeRF, or similar models for photorealistic image generation.
• Strong background indistributed trainingand GPU acceleration (CUDA).
• Familiarity with 3D vision concepts including rendering pipelines, texture mapping, and lighting simulation.
• Experience working with large-scale datasets and performance tuning for model accuracy and efficiency.

Soft Skills

• Excellent analytical and problem-solving abilities.
• Strong communication skills to explain complex architectures and technical decisions.
• Proactive, collaborative, and team-oriented mindset.

Keywords / Tools

Generative AI, Diffusion Models, PyTorch, Computer Vision, Deep Learning, CNN, 3D Vision, Image Processing, Object Detection, CUDA, Stable Diffusion, NeRF, GANs