AI Research Engineer (Multi-Modal & Vision)
Tasks
- Apply knowledge distillation
- Build distributed training workflows
- Compress models for resource constrained environments
- Contribute to open source models and datasets
- Curate and balance multimodal datasets
- Design training pipeline workflows
- Develop evaluation benchmarks
- Evaluate model performance and robustness
- Identify and resolve training pipeline bottlenecks
- Implement post training supervised fine tuning
- Optimize model efficiency for deployment
- Publish research findings
- Train and optimize vision language models
- Use reinforcement learning from human feedback
Perks/Benefits
Skills/Tech-stack
Data Curation | Dataset Filtering | Distributed Training | Efficient Fine Tuning | Evaluation benchmarks | Fine Tuning | GPU Training | Human Feedback | Knowledge Distillation | Language Modeling | Learning from Human Feedback | Model Compression | Model Optimization | Multimodal Learning | Parameter efficient fine-tuning | PyTorch | Python | Reinforcement Learning | Reinforcement Learning from Human Feedback | Supervised Fine Tuning | TensorFlow | Transformers | Vision-language | Vision-language modeling
Education
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