Computer Vision ResearcherJob 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.
