车机 Ai 算法工程师（大模型 / 多模态 / 端侧部署）
Tasks
- Adapt and compress Qwen and other LLM VLM models
- Build multimodal interaction pipelines
- Conduct experiments evaluation and performance analysis
- Deploy and optimize multimodal speech and vision AI
- Develop on device LLM and VLM models
- Document deployment standards and engineering processes
- Export convert quantize and integrate inference engines
- Improve latency throughput memory and power usage
- Optimize CPU GPU NPU inference performance
- Optimize KV Cache and operator fusion
- Perform model lightening with quantization pruning distillation
- Track and validate model compression and acceleration research
Perks/Benefits
- N/A
Skills/Tech-stack
C++ | Deep learning | INT4 Quantization | Inference engine | Int8 Quantization | KV cache | Knowledge Distillation | Language Model | Large Language Model | Linux | MNN | Machine Learning | Model Compression | Model Conversion | Model Tracing | Model export | NCNN | ONNX | ONNX Runtime | Operator fusion | Performance Tuning | Pruning | Python | QAT | QNN | Quantization | SNPE | TFLite | TensorRT | Transformer | Vision Language Model | Vision-language
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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