高级Ai算法部署工程师
Tasks
- Adapt and optimize inference backends for AI accelerators
- Apply model compression and inference optimization
- Build inference pipelines and deploy to edge devices
- Coordinate with model training and chip teams for integration
- Create automated build and packaging scripts
- Deploy LLM and multimodal models
- Develop reusable engineering templates and scaffolding
- Establish verification methods for stable deployment
- Improve code quality and ensure memory safety
- Integrate detection and tracking models into products
- Maintain and evolve on device inference framework
- Optimize inference performance under limited compute memory bandwidth
- Perform cross compilation and on board validation
Perks/Benefits
- N/A
Skills/Tech-stack
ABI compatibility | APU | ARM | Anchor Decoding | Asynchronous callbacks | Batching | Build Automation | C# | C++ | CMake | Cache optimization | Concurrency | Cross-compilation | Cross-platform | Cross-platform deployment | Cross-team | Cross-team integration | DSP | Data Race | Dependency management | ELF | Embedded Linux | Glibc | Inference Optimization | Knowledge Distillation | LLM | Llama.cpp | MIPS | MNN | Memory safety | Model Compression | Multimodal Models | Multithreading | NMS | NPU | Network inference | Neural Network | Neural network inference | ONNX Runtime | Operator fusion | Platform deployment | Pointer Safety | Pruning | Python | Quantization | Scheduling | Shared library | Static Library | Team integration | TensorRT-LLM | Thread synchronization | Tracking Association | UClibc | VLLM
Education
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