Data Scientist - Computer Vision

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.

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.