AI Research Engineer (Multi-Modal & Vision)
Tasks
- Adapt models for resource constrained environments
- Apply knowledge distillation
- Benchmark model performance
- Build distributed training workflows
- Build multimodal datasets
- Compress and optimize models
- Curate and filter datasets
- Design supervised fine tuning
- Develop vision-language models
- Evaluate robustness and real world quality
- Identify and resolve training bottlenecks
- Implement reinforcement learning from human feedback
- Maintain open source AI ecosystems
- Monitor emerging multimodal research
- Optimize and deploy machine learning models
- Prepare research publication submissions
- Train and evaluate multimodal models
- Translate research into engineering improvements
Perks/Benefits
- Autonomy
- Flexible working arrangements
- Fully remote
- Global collaboration opportunities
- Professional growth
- Publication support
- Work-life balance
Skills/Tech-stack
Benchmarking | Computer Vision | Dataset curation | Distributed Training | Efficient Fine Tuning | Evaluation Frameworks | Fine Tuning | GPU Training | Human Feedback | Knowledge Distillation | Language Models | Language Processing | Learning from Human Feedback | Machine Learning | Machine Learning Pipelines | Model Adaptation | Model Compression | Model Optimization | Multimodal Learning | Natural Language | Natural Language Processing | Parameter efficient fine-tuning | PyTorch | Python | Reinforcement Learning | Reinforcement Learning from Human Feedback | Supervised Fine Tuning | TensorFlow | Vision Language Models | Vision-language
Education
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