Data Scientist

About the job

Key Responsibilities • Design, develop, and deploy deep learning models for image classification, object detection, segmentation, pose estimation, OCR, and related tasks. • Work with large-scale datasets (images, videos, annotations), including data cleaning, augmentation, and preprocessing pipelines. • Evaluate and fine-tune models using metrics like IoU, mAP, F1 score, and accuracy. • Conduct research and experimentation with state-of-the-art architectures such as CNNs, Transformers (ViT, DETR), GANs, and self-supervised learning. • Collaborate with cross-functional teams to integrate models into production pipelines (cloud/on-prem). • Stay current with the latest advancements in computer vision and contribute to the company’s innovation roadmap. • Develop tools for model explainability and performance monitoring in production environments. Required Qualifications • B.Tech, Master’s or Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field. • 3+ years of experience in developing and deploying deep learning models for computer vision tasks. • Strong proficiency in Python and deep learning frameworks like PyTorch and TensorFlow. • Hands-on experience with libraries such as OpenCV, Albumentations, MMDetection, Detectron2, or YOLOv5/8. • Experience training and optimizing models on GPU clusters using distributed training (e.g., PyTorch Lightning, DDP). • Familiarity with model deployment (ONNX, TensorRT, TorchScript) and serving (FastAPI, Flask, Triton Inference Server). • Experience with annotation tools (e.g., CVAT, Labelbox) and data versioning tools (e.g., DVC, Weights & Biases). • Strong understanding of computer vision metrics and evaluation protocols. • Strong skillset in mathematical algorithmics and explainability of deep learning models and frameworks. Preferred Skills • Knowledge of 3D vision, SLAM, multi-view geometry and YOLO. • Experience working with video datasets and spatio-temporal models. • Background in self-supervised or semi-supervised learning. • Familiarity with MLOps pipelines and tools like MLflow, Kubeflow, or SageMaker. • Experience in a domain-specific application like medical imaging, aerial imagery, or autonomous vehicles. What We Offer • Competitive compensation and benefits. • Opportunity to work on cutting-edge quantum computing and semiconductor R&D projects. • Collaborative and research-driven work environment. Why Join Us • Work on impactful AI products at the cutting edge of computer vision. • Collaborate with a world-class team of researchers and engineers. • Access to state-of-the-art GPU infrastructure and training platforms. • Flexible work environment with competitive compensation and benefits.