Computer Vision Researcher

Computer Vision Researcher

Job Title

Computer Vision Researcher

Job Summary

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.

Key Responsibilities

• 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.

Required Qualifications

• 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).

Preferred Qualifications

• 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.

Benefits

• 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.