Computer Vision Research Engineer
6 days ago
Experience
2 - 5 Years
Core Responsibilities
- Architect and develop novel deep learning and machine learning neural network architectures, including generative AI models.
- Design, develop, and refine Vision–Language Model (VLM) architectures and integrate them with generative AI techniques for advanced multimodal understanding and generation.
- Spearhead the New Product Development (NPD) process to deliver innovative, differentiated products and solutions that provide clear consumer advantages and exceed market benchmarks.
- Engineer and implement advanced computer vision and deep learning algorithms to address complex scene understanding and multimodal interpretation challenges.
- Analyze experimental visual and contextual data to identify and implement performance improvement strategies.
- Deploy algorithms into operational prototypes and real-time system demonstrations.
- Lead and manage cross-functional teams to ensure seamless integration with related functions.
Job Requirements
- B.Tech or Bachelor's degree in Computer Science, AI, Electrical/Electronic Engineering, or a related discipline.
- 2–5 years of experience in computer vision, generative AI, Vision–Language Models (VLM), multimodal systems, and image processing.
- Strong background in designing image understanding, multimodal, and cross-domain learning algorithms.
- Practical experience deploying real-time Vision–Language Models (VLMs).
- Experience with acceleration frameworks such as NVIDIA TensorRT, DeepStream, and Intel OpenVINO is preferred.
- Proficiency in Python and/or C++.
- Experience containerizing and orchestrating applications with Docker and Kubernetes.
- Knowledge of pattern recognition and Edge AI product development.
- Strong ability to review code, design architectures, and document core components.
- Familiarity with research and development practices and industry standards.
- Preferred: Hands-on experience with Natural Language Processing (NLP) and integrating visual and textual signals for multimodal systems.
Skills: algorithms,deep learning,computer vision,generative ai,image processing
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