AI Research Engineer (Multi-Modal & Vision)

Overview

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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