AI Research Engineer (Multi-Modal & Vision)
Tasks
- Apply knowledge distillation
- Build multimodal datasets
- Compress and adapt models for deployment
- Contribute to open source AI ecosystems
- Create evaluation frameworks
- Deploy multimodal AI systems
- Develop benchmarking systems
- Develop vision-language models
- Implement distributed training workflows
- Implement supervised fine tuning
- Monitor and resolve training performance bottlenecks
- Optimize model efficiency and scalability
- Publish research findings
- Train, evaluate, and optimize models
- Use reinforcement learning from human feedback
Perks/Benefits
- Flexible working arrangements
- Fully remote
- Global collaboration opportunities
- High autonomy
- Professional growth opportunities
- Publication support
Skills/Tech-stack
Benchmarking | Dataset curation | Deep learning | Distributed Training | Efficient Fine Tuning | Evaluation Frameworks | Fine Tuning | GPU Computing | Human Feedback | Knowledge Distillation | Language Models | Machine Learning | Model Compression | Model Optimization | Multimodal Learning | Open Source | Parameter efficient fine-tuning | Reinforcement Learning | Supervised Fine Tuning | Vision Language Models | Vision-language
Education
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