Computer Vision Engineer

We are seeking experienced Multimodal and Vision AI Engineers/Scientists to research, develop, optimize, and deploy Vision-Language Models (VLMs), multimodal generative models, diffusion models, and traditional computer vision techniques. You will work on foundational models integrating vision, language, and audio, optimize AI architectures, and push the boundaries of multimodal AI research.

Responsibilities:

• Research, design, and train multimodal vision-language models (VLMs), integrating deep learning, transformers, and attention mechanisms.
• Develop and optimize small-scale distillation of VLMs for efficient deployment on resource-constrained devices.
• Implement state-of-the-art object detection (YOLO, Faster R-CNN), segmentation (Panoptic Segmentation), classification (ResNets, Vision Transformers), and image generation (Stable Diffusion, Stable Cascade).
• Train or fine-tune vision models for representation (e.g., Vision Transformers, Q-Former, CLIP, SigLIP), generation, and video representation (e.g., Video-Swin Transformer).
• Work with diffusion models and generative models for conditional image generation and multimodal applications.
• Optimize CNN-based architectures for computer vision tasks like recognition, tracking, and feature extraction.
• Implement and optimize audio models for representation (e.g., W2V-BERT) and generation (e.g., Hi-Fi GAN, SeamlessM4T).
• Innovate with multimodal fusion techniques such as early fusion, deep fusion, Mixture-of-Experts (MoE), FlashAttention, MQA, GQA, MLA, and other transformer architectures.
• Advance video analysis, video summarization, and video question-answering models to enhance multimedia understanding.
• Integrate and tailor deep learning frameworks like PyTorch, TensorFlow, DeepSpeed, Lightning, Habana, and FSDP.
• Deploy large-scale distributed AI models using MLOps frameworks such as AirFlow, MosaicML, Anyscale, Kubeflow, and Terraform.
• Publish research in top-tier conferences (NeurIPS, CVPR, ICCV, ICLR, ICML) and contribute to open-source AI projects.

Qualifications:

• Ph.D. or Master’s degree with 2 years of experience in Vision-Language Models (VLMs), multimodal AI, diffusion models, CNNs, ResNets, computer vision, and generative models.
• Demonstrated expertise in high-performance computing, proficiency in Python, C/C , CUDA, and kernel-level programming for AI applications.
• Experience in optimizing training and inference of large-scale AI models, with knowledge of quantization, distillation, and LLMOps.
• Hands-on experience with object detection (YOLO, Faster R-CNN), image segmentation (Panoptic Segmentation), and video understanding (Swin Transformer, Timesformer).
• Proficiency in AI toolkits like PyTorch, TensorFlow, OpenCV, and familiarity with MLOps frameworks.