AI Research Engineer (Kernel & Inference Optimization)
Tasks
- Build inference pipelines
- Design model serving architectures
- Develop evaluation frameworks
- Diagnose serving bottlenecks
- Implement distributed inference techniques
- Integrate inference frameworks into production pipelines
- Monitor performance metrics
- Optimize inference strategies
- Optimize latency and throughput
- Optimize memory usage
- Prepare test datasets and simulation scenarios
- Run inference tests
Perks/Benefits
Skills/Tech-stack
Compute Shaders | Diffusion Models | Distributed inference | Edge Computing | Expert parallelism | Flash Attention | GPU Kernels | GPU clusters | High Throughput | Inference Optimization | KV cache | Low Latency | Machine Learning | Memory Optimization | Metal Shading Language | Mobile optimization | Model Serving | NLP | NLP Research | On-device Inference | Pipeline parallelism | Pruning | Quantization | Shading language | Speculative decoding | Tensor Parallelism | Vision Transformers
Education
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