Computer Vision Researcher

We are seeking a highly skilled Computer Vision Researcher to develop cutting-edge computer vision and deep learning solutions for real-world applications. The ideal candidate will have strong expertise in image processing, deep learning, vision transformers, object detection, image segmentation, and generative vision models. You will conduct research, build state-of-the-art models, and collaborate with engineering teams to deploy scalable computer vision solutions., * Conduct research and develop advanced computer vision algorithms for image and video understanding.

• Design, implement, and optimize deep learning models for tasks such as image classification, object detection, semantic and instance segmentation, pose estimation, optical character recognition (OCR), tracking, and video analytics.
• Develop and fine-tune vision foundation models, vision transformers (ViTs), and multimodal vision-language models for domain-specific applications.
• Build scalable training, evaluation, and inference pipelines for computer vision systems.
• Work with large-scale image and video datasets, including data collection, annotation, augmentation, and quality assessment.
• Evaluate model performance using industry-standard benchmarks and metrics, and optimize models for accuracy, latency, and efficiency.
• Collaborate with machine learning engineers, data scientists, software engineers, and product teams to transition research into production.
• Stay up to date with the latest advancements in computer vision, deep learning, and generative AI by reviewing research publications and implementing relevant techniques.

• Master’s or Ph.D. in Computer Science, Artificial Intelligence, Computer Vision, Machine Learning, Electrical Engineering, or a related field.
• 3 years of experience in computer vision research or applied machine learning.
• Strong understanding of:
• Image Processing
• Deep Learning
• Convolutional Neural Networks (CNNs)
• Vision Transformers (ViTs)
• Object Detection
• Image Segmentation
• Feature Extraction
• Representation Learning
• Self-Supervised Learning
• Proficiency in Python.
• Hands-on experience with PyTorch or TensorFlow.
• Experience with computer vision libraries such as OpenCV, Detectron2, MMDetection, Ultralytics YOLO, or OpenMMLab.
• Strong understanding of model evaluation metrics, including mAP, IoU, precision, recall, and F1-score.
• Experience with GPU acceleration, distributed training, and model optimization.
• Familiarity with Git, Docker, Linux, and cloud platforms (AWS, Azure, or Google Cloud)., * Experience with vision foundation models such as Segment Anything Model (SAM), DINOv2, CLIP, Florence, or Grounding DINO.
• Experience with OCR, document AI, medical imaging, satellite imagery, autonomous driving, robotics, or industrial inspection.
• Knowledge of 3D computer vision, depth estimation, point cloud processing, SLAM, or neural rendering.
• Experience with generative AI models for image synthesis and editing.
• Publications in leading AI conferences such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, or WACV.
• Contributions to open-source computer vision projects.

Technical Skills

• Python
• PyTorch / TensorFlow
• OpenCV
• Detectron2
• MMDetection
• Ultralytics YOLO
• Hugging Face Transformers
• CUDA
• DeepSpeed
• NumPy
• Pandas
• Git
• Docker
• Kubernetes (preferred)
• Linux
• SQL
• AWS / Azure / Google Cloud

Soft Skills

• Strong analytical and research skills.
• Excellent problem-solving and debugging abilities.
• Effective communication and technical documentation skills.
• Ability to collaborate with cross-functional teams.
• Curiosity, innovation, and a passion for advancing computer vision research.

Nice to Have

• Experience with multimodal AI and vision-language models.
• Familiarity with large language models (LLMs) and retrieval-augmented generation (RAG).
• Experience with MLOps, model deployment, and CI/CD pipelines.
• Knowledge of reinforcement learning for vision-based decision-making.
• Experience with synthetic data generation and simulation environments.

• Competitive salary and performance-based incentives.
• Flexible work arrangements.
• Comprehensive health and wellness benefits.
• Learning, certification, and conference sponsorship opportunities.
• Access to high-performance GPU infrastructure.
• Opportunity to work on cutting-edge computer vision research and production AI systems.