AI Research Engineer (Multi Modal & Vision)📣 إعلان

About the AI Research Engineer Role

Jobgether is seeking an experienced AI Research Engineer with a focus on multimodal and vision capabilities. This full-time position involves contributing to the development of advanced AI systems, covering the entire model lifecycle from data preparation and training to evaluation and deployment. The role offers significant ownership and the opportunity to apply state-of-the-art techniques to complex challenges within a collaborative team environment.

Core Responsibilities

• Conduct end-to-end research and development of vision-language models, including training, evaluation, optimization, and deployment.
• Design and implement advanced post-training methodologies such as supervised fine-tuning, knowledge distillation, and reinforcement learning from human feedback.
• Build, curate, filter, and maintain high-quality multimodal datasets tailored to domain-specific applications.
• Improve model efficiency and scalability through optimization, compression, and adaptation techniques suitable for resource-constrained environments.
• Develop benchmarking systems and evaluation frameworks to assess model quality, robustness, and real-world performance.
• Build and maintain distributed training workflows across GPU infrastructure while identifying and resolving performance bottlenecks.
• Contribute to open-source AI ecosystems by leveraging and enhancing models, datasets, and development tools.
• Monitor emerging research in multimodal learning and vision-language systems, translating relevant advancements into practical improvements.
• Collaborate on research publications and contribute to scientific advancements through conference or journal submissions.

Required Qualifications and Experience

• Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field; Master's or PhD is preferred.
• 5-10 years of experience in AI research and development.
• Strong hands-on experience working with multimodal AI systems, particularly vision-language models.
• Proven expertise in supervised fine-tuning, knowledge distillation, reinforcement learning from feedback, and other post-training optimization techniques.
• Experience with parameter-efficient fine-tuning approaches and distributed training frameworks.
• Demonstrated success improving model performance on industry-standard benchmarks or production use cases.
• Strong understanding of model optimization techniques for deployment in resource-constrained environments.
• Experience building scalable machine learning pipelines and training workflows on GPU infrastructure.
• Proven contributions to open-source multimodal AI projects through platforms such as GitHub or Hugging Face.
• Research background supported by publications in leading AI conferences or journals is highly desirable.

Essential Skills and Attributes

• Strong analytical thinking and problem-solving skills.
• Ability to balance research innovation with production-oriented engineering practices.
• Excellent communication skills.
• Ability to collaborate effectively within distributed, cross-functional teams.

Work Environment and Type

This is a full-time position. You will be working within a highly skilled and collaborative team environment, contributing to the development of AI solutions designed for real-world production.