AI Research Engineer (Multi-Modal & Vision)

Tasks

• Apply reinforcement learning from human feedback
• Build distributed training workflows
• Build multimodal datasets
• Compress and adapt models for deployment
• Contribute to open source AI ecosystems
• Create benchmarking and evaluation frameworks
• Design supervised fine tuning
• Develop vision language models end to end
• Implement knowledge distillation
• Monitor and resolve training performance bottlenecks
• Optimize model efficiency and scalability
• Support research publications
• Translate multimodal research into production improvements

Perks/Benefits

• Autonomy and ownership
• Flexible working arrangements
• Fully remote work
• Professional growth opportunities
• Publication support
• Work-life balance

Skills/Tech-stack

Benchmarking | Deep learning | Distributed Training | Efficient Fine Tuning | Evaluation Frameworks | Fine Tuning | GPU infrastructure | Human Feedback | Knowledge Distillation | Language Models | Learning from Human Feedback | Model Compression | Model Optimization | Multimodal AI | Parameter efficient fine-tuning | Reinforcement Learning | Reinforcement Learning from Human Feedback | Supervised Fine Tuning | Vision Language Models | Vision-language