Backend Inference Runtime Engineer Graduate (AML Inference) - 2027 Start
Tasks
- Apply operator fusion and compilation optimization
- Benchmark against TensorRT-LLM
- Benchmark against vLLM
- Design and develop distributed parallel inference solutions
- Enhance GPU NPU hardware adaptability
- Implement mixture of experts expert parallelism
- Implement pipeline parallelism
- Implement sequence parallelism
- Implement tensor parallelism
- Improve end to end GPU performance
- Iterate on inference performance and cost efficiency
- Optimize GPU memory access
- Optimize large model inference engine architecture
- Reduce cross card communication overhead
- Tune computing pipeline and asynchronous scheduling
Perks/Benefits
- N/A
Skills/Tech-stack
Asynchronous Scheduling | Benchmarking | Cache optimization | Compilation Optimization | Distributed Systems | GPU Computing | GPU Memory Optimization | GPU Performance | GPU Performance Optimization | GPU memory | High Performance | High-Performance Computing | Language Models | Large Language Models | Machine Learning | Memory Optimization | Mixture of Experts | NPU | Operator fusion | Performance Computing | Performance optimization | Pipeline parallelism | Sequence parallelism | Tensor Parallelism | TensorRT-LLM | VLLM
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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