AI Research Engineer (Multi-Modal & Vision)
Tasks
- Apply knowledge distillation
- Build and curate multimodal datasets
- Collaborate on research publications
- Conduct vision language model research and development
- Develop model benchmarking and evaluation frameworks
- Implement reinforcement learning from human feedback
- Improve model efficiency through optimization and compression
- Monitor multimodal learning research and apply improvements
- Optimize and deploy machine learning models
- Perform supervised fine-tuning
- Run distributed training workflows on GPU infrastructure
- Train and evaluate multimodal models
Perks/Benefits
- Autonomy
- Flexible working arrangements
- Fully remote
- Global collaboration opportunities
- Professional growth
- Publication support
- Work-life balance
Skills/Tech-stack
Benchmarking | Computer Vision | Dataset curation | Deep learning | Distributed Training | Efficient Fine Tuning | Fine Tuning | GPU Computing | Human Feedback | Knowledge Distillation | Language Models | Language Processing | Learning from Human Feedback | Machine Learning | Model Compression | Model Optimization | Multimodal Learning | Natural Language | Natural Language Processing | Parameter efficient fine-tuning | PyTorch | Python | Reinforcement Learning | Reinforcement Learning from Human Feedback | Supervised Fine Tuning | Vision Language Models | Vision-language
Education
Roles
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