AI Research Engineer (Multi-Modal & Vision)

Tasks

• Apply reinforcement learning from human feedback
• Build multimodal datasets
• Collaborate on research publications
• Conduct end to end research and development of vision language models
• Contribute to open source AI ecosystems
• Design supervised fine tuning workflows
• Develop model evaluation frameworks
• Identify and resolve GPU performance bottlenecks
• Implement distributed training workflows
• Implement knowledge distillation methods
• Optimize model efficiency and scalability
• Translate emerging research into production improvements

Perks/Benefits

• Autonomy
• Flexible working arrangements
• Publication support
• Remote work
• Work-life balance

Skills/Tech-stack

Benchmarking | Computer Vision | Distributed Training | Efficient Fine Tuning | Evaluation Frameworks | Fine Tuning | GPU Computing | Human Feedback | Knowledge Distillation | Language Models | Language Processing | Learning from Human Feedback | Model Compression | Model Optimization | Multimodal Learning | Natural Language | Natural Language Processing | Open Source | Parameter efficient fine-tuning | Reinforcement Learning | Reinforcement Learning from Human Feedback | Supervised Fine Tuning | Training pipelines | Vision Language Models | Vision-language