Job
Description
Role Overview: As a Senior Machine Learning Engineer at TrueFan, you will play a crucial role in designing, developing, and deploying cutting-edge models for end-to-end content generation. Your focus will be on AI-driven image/video generation, lipsyncing, and multimodal AI systems using deep generative modeling techniques. You will contribute to creating highly realistic and controllable AI-generated media while staying updated with the latest trends in the field. Key Responsibilities: - Research & Develop: Design and implement state-of-the-art generative models such as Diffusion Models, 3D VAEs, and GANs for AI-powered media synthesis. - End-to-End Content Generation: Build and optimize AI pipelines for high-fidelity image/video generation and lipsyncing using diffusion and autoencoder models. - Speech & Video Synchronization: Develop advanced lipsyncing and multimodal generation models integrating speech, video, and facial animation. - Real-Time AI Systems: Implement and optimize models for real-time content generation and interactive AI applications using efficient model architectures. - Scaling & Production Deployment: Collaborate with software engineers to deploy models efficiently on cloud-based architectures (AWS, GCP, or Azure). - Collaboration & Research: Stay ahead of the latest trends in deep generative models and transformer-based vision systems to enhance AI-generated content quality. - Experimentation & Validation: Design and conduct experiments to evaluate model performance, improve fidelity, realism, and computational efficiency. - Code Quality & Best Practices: Participate in code reviews, improve model efficiency, and document research findings for team knowledge-sharing. Qualifications: - Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field. - 3+ years of experience working with deep generative models like Diffusion Models, 3D VAEs, GANs, and autoregressive models. - Strong proficiency in Python and deep learning frameworks such as PyTorch. - Expertise in multi-modal AI, text-to-image, image-to-video generation, and audio to lipsync. - Strong understanding of machine learning principles, statistical methods, and problem-solving abilities. - Good to have experience in real-time inference optimization, cloud deployment, and distributed training. - Familiarity with generative adversarial techniques, reinforcement learning for generative models, and large-scale AI model training. Preferred Qualifications: - Experience with transformers and vision-language models like CLIP, BLIP, GPT-4V. - Background in text-to-video generation, lipsync generation, and real-time synthetic media applications. - Experience in cloud-based AI pipelines (AWS, Google Cloud, or Azure) and model compression techniques. - Contributions to open-source projects or published research in AI-generated content, speech synthesis, or video synthesis. Role Overview: As a Senior Machine Learning Engineer at TrueFan, you will play a crucial role in designing, developing, and deploying cutting-edge models for end-to-end content generation. Your focus will be on AI-driven image/video generation, lipsyncing, and multimodal AI systems using deep generative modeling techniques. You will contribute to creating highly realistic and controllable AI-generated media while staying updated with the latest trends in the field. Key Responsibilities: - Research & Develop: Design and implement state-of-the-art generative models such as Diffusion Models, 3D VAEs, and GANs for AI-powered media synthesis. - End-to-End Content Generation: Build and optimize AI pipelines for high-fidelity image/video generation and lipsyncing using diffusion and autoencoder models. - Speech & Video Synchronization: Develop advanced lipsyncing and multimodal generation models integrating speech, video, and facial animation. - Real-Time AI Systems: Implement and optimize models for real-time content generation and interactive AI applications using efficient model architectures. - Scaling & Production Deployment: Collaborate with software engineers to deploy models efficiently on cloud-based architectures (AWS, GCP, or Azure). - Collaboration & Research: Stay ahead of the latest trends in deep generative models and transformer-based vision systems to enhance AI-generated content quality. - Experimentation & Validation: Design and conduct experiments to evaluate model performance, improve fidelity, realism, and computational efficiency. - Code Quality & Best Practices: Participate in code reviews, improve model efficiency, and document research findings for team knowledge-sharing. Qualifications: - Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field. - 3+ years of experience working with deep generative models like Diffusion Models, 3D VAEs, GANs, and autoregressive models. - Strong proficiency in Python and deep learning frameworks such as PyTorch. - Expertise in multi-modal AI, text-to-image,
