AI Research Engineer (Multi-Modal & Vision)
Tasks
- Adapt models for resource constrained environments
- Apply reinforcement learning from human feedback
- Build evaluation frameworks and benchmarks
- Conduct supervised fine-tuning
- Contribute to open source multimodal ecosystems
- Curate multimodal datasets
- Design training pipelines for vision language systems
- Identify training pipeline bottlenecks
- Implement knowledge distillation
- Improve model efficiency for deployment
- Publish research findings
- Scale training workflows on distributed GPU infrastructure
- Train and optimize vision language models
Perks/Benefits
Skills/Tech-stack
Benchmarking | Computer Vision | Dataset curation | Distributed Training | Efficient Fine Tuning | Evaluation metrics | Fine Tuning | Git | Huggingface | Human Feedback | Knowledge Distillation | Language Models | Language Processing | Learning from Human Feedback | Mixed Precision | Model Compression | Multimodal Learning | Natural Language | Natural Language Processing | Parameter efficient fine-tuning | PyTorch | Python | Quantization | Reinforcement Learning | Reinforcement Learning from Human Feedback | Supervised Learning | Transformers | Vision Language Models | Vision-language
Education
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