AI Research Engineer (Multi-Modal & Vision)
Tasks
- Apply knowledge distillation
- Build distributed training workflows
- Build training evaluation optimization and deployment pipelines
- Collaborate on research publications
- Contribute to open source AI ecosystems
- Create curate and maintain multimodal datasets
- Develop benchmarking systems and evaluation frameworks
- Develop vision language models end to end
- Identify and resolve GPU performance bottlenecks
- Implement reinforcement learning from human feedback
- Monitor emerging multimodal research and translate into improvements
- Optimize compress and adapt models for efficient deployment
- Perform supervised fine-tuning
Perks/Benefits
- Flexible working arrangements
- Fully remote
- High autonomy
- Professional growth
- Publication support
- Work-life balance
Skills/Tech-stack
Benchmarking | Deep learning | Distributed Training | Efficient Fine Tuning | Fine Tuning | GPU Computing | Human Feedback | Knowledge Distillation | Language Models | Learning from Human Feedback | Machine Learning | Machine Learning Pipelines | Model Compression | Model Optimization | Parameter efficient fine-tuning | PyTorch | Python | Reinforcement Learning | Reinforcement Learning from Human Feedback | Supervised Fine Tuning | Vision Language Models | Vision-language
Education
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