AI Research Engineer (Multi-Modal & Vision)
Tasks
- Build and scale training workflows across distributed GPUs
- Build evaluation frameworks and benchmarks
- Conduct end to end research on vision language models
- Contribute to open source AI ecosystems
- Design and implement post training pipelines
- Develop and curate multimodal datasets
- Drive model efficiency and deployability
- Identify and resolve training pipeline bottlenecks
- Publish research in top AI conferences
- Translate research into production improvements
Perks/Benefits
Skills/Tech-stack
Benchmarking | Data Balancing | Data Filtering | Dataset curation | Distributed GPU | Distributed GPU training | Distributed Training | Efficient Fine Tuning | Evaluation Frameworks | Fine Tuning | GPU Training | GitHub | Huggingface | Human Feedback | Knowledge Distillation | Language Models | Large Language Models | Learning from Human Feedback | Model Compression | Model Optimization | Multimodal Learning | Parameter efficient fine-tuning | Reinforcement Learning | Reinforcement Learning from Human Feedback | Resource-constrained deployment | Supervised Fine Tuning | Vision Language Models | Vision-language
Education
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