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Salary not listed
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About the role
Responsibilities
Quantize, prune and convert models for deployment
Port models to Quadric platform using Quadric toolchain
Optimize inference deployment for latency, speed
Benchmark and profile model performance and accuracy
Collaborate across related areas of the AI inference stack to support team and business priorities
Develop tools to scale and speed up the deployment
Make improvements to SDK and runtime
Provide technical support and documents to customers and developer community
Requirements
Bachelor’s or Master’s in Computer Science and/or Electrical Engineering
5+ years of experience in AI/LLM model inference and deployment frameworks/tools
Experience with model quantization (PTQ, QAT) and tools
Experience with model accuracy measures
Experience with model inference performance profiling
Experience with at least one of the following frameworks: onnxruntime, Pytorch, vLLM, huggingface-transformer, neural-compressor, llamacpp
Proficiency in C/C++ and Python
Demonstrate good capability in problem solving, debug and communication
Skills
Model QuantizationPtqQatOnnx RuntimePyTorchvLLMHugging Face TransformersNeural CompressorLlama.CppC++PythonModel ProfilingBenchmarkingAi InferenceLlm Deployment
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