AI Research Engineer (Multi-Modal & Vision)
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Key skills for this role
About the Role
Jobgether is hiring an AI Research Engineer (Multi-Modal & Vision) for a partner company in UAE. The role involves end-to-end research and development of vision-language models, including training, evaluation, optimization, and deployment.
Key Skills for This Role
Responsibilities
- Conduct end to end research and development of vision language models, including training, evaluation, optimization, and deployment activities
- 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 when appropriate
Requirements
- Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, or related field; Master's or PhD preferred
- 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
- Excellent communication skills and ability to collaborate effectively within distributed, cross functional teams
Full Job Posting
Position Overview
- This is an exciting opportunity for a research focused AI engineer to contribute to the development of advanced multimodal systems that combine vision and language capabilities.
- The role covers the full AI model lifecycle, from dataset creation and training pipeline development to model evaluation, optimization, and deployment.
- Working within a highly skilled and collaborative team, you will help build scalable AI solutions designed for real world production environments.
Accountabilities
- Conduct end to end research and development of vision language models, including training, evaluation, optimization, and deployment activities.
- 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 when appropriate.
Requirements
- Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field; Master's or PhD preferred.
- 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.
- Strong analytical thinking, problem solving skills, and the ability to balance research innovation with production oriented engineering practices.
- Excellent communication skills and the ability to collaborate effectively within distributed, cross functional teams.
Benefits
- Competitive salary package aligned with experience and expertise.
- Opportunity to work on cutting edge multimodal AI and vision language research projects.
- Fully remote work environment with global collaboration opportunities.
- Exposure to large scale AI infrastructure and advanced machine learning technologies.
- High degree of autonomy, ownership, and impact on product and research outcomes.
- Collaboration with experienced researchers, engineers, and AI specialists.
- Professional growth opportunities through research, innovation, and publication support.
- Flexible working arrangements that support work life balance.
- Dynamic, fast paced environment focused on innovation and continuous learning.
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