AI Research Engineer (Multi-Modal & Vision)
Tasks
- Apply knowledge distillation
- Apply reinforcement learning from human feedback
- Build distributed training workflows
- Build multimodal datasets
- Compress and adapt models
- Contribute to research publications
- Develop benchmarking systems
- Develop vision-language models
- Evaluate model robustness and performance
- Implement supervised fine tuning
- Maintain open source AI ecosystems
- Monitor and resolve training bottlenecks
- Optimize model efficiency and scalability
- Train evaluate optimize deploy models
Perks/Benefits
- Flexible working arrangements
- High autonomy
- Professional growth
- Publication support
- Remote work
- Work-life balance
Skills/Tech-stack
Benchmarking | Computer Vision | Dataset curation | Deep learning | Distributed Training | Efficient Fine Tuning | Evaluation Frameworks | Fine Tuning | GPU Computing | Human Feedback | Knowledge Distillation | Language Processing | Learning from Human Feedback | Machine Learning | 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 | Vision-language
Education
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