Intern - GenAI Benchmarking (MLE) / On-Device Model Deployment (SWE)
Seoul, Korea, Republic of
KRW 45000K-55000K (estimate) Entry-level Internship
Tasks
- Apply quantization workflows
- Benchmark generative model outputs
- Define evaluation strategies and KPIs
- Enable static shape graph export
- Implement efficient inference techniques
- Implement on device inference optimizations
- Improve reliability and efficiency of evaluation
- Maintain evaluation and deployment codebases
- Optimize KV cache management
- Restructure model architectures for edge deployment
- Validate accuracy of quantized models
- Write tested documented code
Perks/Benefits
- N/A
Skills/Tech-stack
AIMet | Automated testing | Cache Management | Code review | Context modeling | DSP | Device deployment | Edge Computing | Executorch | Experiment reproducibility | GPU | Git | Group Wise Weight Quantization | Hugging Face | Hugging Face Transformers | KV cache | KV-cache management | Llama.cpp | Long Context | Long Context Modeling | Mixed Precision | NPU | ONNX Runtime | On-device Deployment | Post-training | Post-training Quantization | Pruning | PyTorch | Python | Quantization | Speculative decoding | Static Shape Export | Statistical Analysis | TPU
Education
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