AI Research Engineer (Multi-Modal & Vision)
Tasks
- Apply knowledge distillation
- Benchmark model robustness
- Build evaluation frameworks
- Build vision-language models
- Compress and quantize models
- Contribute to open source models and datasets
- Create multimodal datasets
- Curate and filter datasets
- Deploy AI models
- Design supervised fine tuning
- Evaluate model quality
- Identify training performance bottlenecks
- Implement distributed training workflows
- Maintain GPU training pipelines
- Monitor multimodal research
- Optimize model efficiency and scalability
- Optimize model performance
- Prepare research publications
- Train multimodal models
- Train reinforcement learning from human feedback
- Translate research into production improvements
Perks/Benefits
Skills/Tech-stack
Benchmarking | Computer Vision | Distributed Training | Efficient Fine Tuning | Evaluation Frameworks | Fine Tuning | GPU Computing | Human Feedback | Knowledge Distillation | Language Models | Language Processing | Learning from Human Feedback | Machine Learning | Machine Learning Pipelines | Model Compression | Model Optimization | Model Quantization | 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 Models | Vision-language
Education
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