AI Visual Language Software Engineer

Izisurf is an emerging technology company focused on building advanced AI systems that understand and generate visual and linguistic information in integrated ways. The team combines expertise in machine learning, software engineering, and user-centered design to create scalable, production-ready AI solutions. Izisurf values experimentation, clear communication, and reliable engineering practices to deliver impactful products. The organization supports remote collaboration and encourages continuous learning and professional growth.

The AI Visual Language Software Engineer is a full-time remote role responsible for designing, implementing, and optimizing models that connect visual data with natural language. Day-to-day responsibilities include developing and maintaining software components for pattern recognition and neural network-based systems, building and refining NLP pipelines, and integrating these models into production services and applications. The role involves writing clean, tested code, conducting experiments to evaluate model performance, and collaborating with product and research teams to translate requirements into technical solutions. The engineer will also monitor system reliability, optimize runtime performance, and contribute to documentation and code reviews to ensure quality and maintainability.

As an AI Visual Language Software Engineer, you will play a key role in developing next-generation multimodal AI systems that combine computer vision and natural language understanding. You will collaborate closely with AI researchers, software engineers, and product teams to design, train, optimize, and deploy production-grade AI models capable of interpreting images, videos, and other visual data alongside textual information.

• Design, develop, and deploy multimodal AI models integrating computer vision and natural language processing.
• Build scalable machine learning pipelines for training, evaluation, inference, and continuous improvement.
• Develop production-ready software using Python and modern deep learning frameworks such as PyTorch or TensorFlow.
• Optimize model accuracy, inference latency, memory usage, and GPU performance for real-world deployment.
• Integrate AI models into cloud services, APIs, edge devices, and enterprise software platforms.
• Implement robust testing, monitoring, logging, and CI/CD practices for machine learning applications.
• Collaborate with cross-functional teams to translate business requirements into technical solutions.
• Conduct experiments, benchmark model performance, and document research findings.
• Participate in architecture discussions, design reviews, and code reviews while maintaining high engineering standards.
• Stay current with advancements in Vision-Language Models (VLMs), Large Language Models (LLMs), computer vision, and multimodal learning.

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• Strong foundation in Computer Science and Software Development, including data structures, algorithms, and version control.
• Experience with Pattern Recognition and Neural Networks, preferably in visual or multimodal AI settings.
• Hands-on experience with Natural Language Processing (NLP), including model training, evaluation, and deployment.
• Proficiency in Python and modern machine learning frameworks such as PyTorch or TensorFlow.
• Understanding of distributed systems, REST APIs, microservices, and cloud platforms (AWS, GCP, or Azure).
• Experience with Linux development environments, Git, Docker, and software engineering best practices.
• Ability to work independently in a remote environment while communicating effectively with cross-functional teams.
• Bachelor's or higher degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience.

• Experience with Vision-Language Models (VLMs), Large Language Models (LLMs), or multimodal foundation models.
• Experience with computer vision techniques such as object detection, segmentation, OCR, depth estimation, or visual localization.

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• Familiarity with transformer architectures, attention mechanisms, diffusion models, or generative AI.
• Experience deploying AI models using Kubernetes, Docker, ONNX, TensorRT, or NVIDIA CUDA.
• Knowledge of MLOps tools, distributed training, model serving, and large-scale data processing.
• Experience working with edge AI devices such as NVIDIA Jetson, embedded GPUs, or robotics platforms.
• Contributions to open-source AI projects or published research are a plus.

• Fully remote work environment.
• Opportunity to work on cutting-edge multimodal AI technology.
• Collaborative and research-driven engineering culture.
• Flexible working hours.
• Professional development and continuous learning opportunities.
• Competitive compensation based on experience.
• Opportunity to contribute to products deployed in real-world production environments.