Backend Inference Runtime Engineer Graduate (AML Inference) - 2027 Start
Tasks
- Adapt inference engine for GPU and NPU hardware
- Benchmark against vLLM and TensorRT-LLM
- Develop distributed parallel inference solutions
- Implement and iterate global large model inference solutions
- Implement asynchronous stream scheduling
- Implement mixture of experts expert parallelism
- Implement pipeline parallelism
- Implement sequence parallelism
- Implement tensor parallelism
- Improve GPU performance through operator fusion and compilation optimization
- Optimize GPU memory access and compute pipeline
- Optimize inference load balancing and parallel efficiency
- Optimize large model inference engine architecture
- Reduce cross card communication overhead
Perks/Benefits
- N/A
Skills/Tech-stack
Activation functions | C# | C++ | CUDA | Computation Graph | Computation graph optimization | Constant folding | Deep learning | Distributed inference | GPU Memory Model | GPU Performance | GPU Programming | GPU memory | GPU performance analysis | Graph optimization | Matrix Operations | Memory Reuse | Memory model | Mixture of Experts | Normalization | Nsight | Operator fusion | Performance Analysis | Pipeline parallelism | Precision alignment | Profiling | Python | Quantization | SGLang | Scheduling optimization | Sequence parallelism | Tensor Parallelism | TensorRT-LLM | VLLM | Vectorization
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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