AI Research Engineer (Multi-Modal & Vision)
Tasks
- Build and scale training workflows on distributed GPU infrastructure
- Conduct end to end research and engineering on vision language models
- Contribute to open source multimodal ecosystems
- Design and implement evaluation frameworks and benchmarks
- Design and implement post training pipelines
- Develop and maintain multimodal datasets
- Drive model efficiency and deployability
- Identify and resolve bottlenecks in training pipelines
- Publish research findings in top AI conferences and journals
- Stay current with multimodal learning research
Perks/Benefits
Skills/Tech-stack
Computer Vision | Data Balancing | Data Filtering | Dataset curation | Distributed Training | Efficient Fine Tuning | Evaluation & benchmarking | Fine Tuning | GPU Computing | Human Feedback | Knowledge Distillation | Language Models | Language Processing | Learning from Human Feedback | Machine Learning | Model Compression | Model Optimization | Multimodal Learning | Natural Language | Natural Language Processing | Open Source | Open-source tools | Parameter efficient fine-tuning | Reinforcement Learning | Reinforcement Learning from Human Feedback | Supervised Fine Tuning | Vision Language Models | Vision-language
Education
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