AI Research Engineer (Multi-Modal & Vision)
Tasks
- Apply knowledge distillation methods
- Build supervised fine tuning pipelines
- Conduct vision language model research
- Contribute to research publications
- Create and curate multimodal datasets
- Develop benchmarking and evaluation frameworks
- Implement reinforcement learning from human feedback
- Improve model efficiency through optimization and compression
- Monitor multimodal research and translate into improvements
- Optimize and deploy vision language models
- Run distributed training workflows on GPU infrastructure
- Train and evaluate multimodal models
Perks/Benefits
- Flexible working arrangements
- Global collaboration
- Publication support
- Remote work
- Research innovation opportunities
Skills/Tech-stack
Benchmarking | Deep learning | Distributed Training | Efficient Fine Tuning | Evaluation Frameworks | Fine Tuning | GPU infrastructure | GitHub | Hugging Face | Human Feedback | Knowledge Distillation | Learning from Human Feedback | Machine Learning | Machine Learning Pipelines | Model Compression | Model Optimization | Multimodal AI | Open Source | Open-source machine learning | Parameter efficient fine-tuning | Reinforcement Learning | Reinforcement Learning from Human Feedback | Supervised Fine Tuning | Vision-language
Education
Roles
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