AI Research Engineer (Multi-Modal & Vision)
Tasks
- Build and maintain multimodal datasets
- Collaborate on research publications
- Conduct end to end research and development of vision language models
- Contribute to open source AI ecosystems
- Design and implement supervised fine-tuning
- Develop model benchmarking and evaluation frameworks
- Identify and resolve training performance bottlenecks
- Implement distributed training workflows on GPU infrastructure
- Implement knowledge distillation
- Optimize and compress models for deployment
- Train models using reinforcement learning from human feedback
- Translate research into production improvements
Perks/Benefits
- Autonomy
- Flexible working arrangements
- Professional growth
- Publication support
- Remote work
- Work-life balance
Skills/Tech-stack
Benchmarking | Dataset curation | Distributed Training | Efficient Fine Tuning | Fine Tuning | GPU Computing | Hugging Face | Human Feedback | Knowledge Distillation | Learning from Human Feedback | Machine Learning | Machine Learning Pipelines | Model Compression | Model Evaluation | Model Optimization | Multimodal Learning | Open Source | Open source contribution | Parameter efficient fine-tuning | Reinforcement Learning | Reinforcement Learning from Human Feedback | Supervised Fine Tuning | Vision-language
Education
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