AI Inference Engineer - Model Optimization & Deployment
Tasks
- Accelerate inference with mixed precision
- Benchmark accuracy and latency
- Build TensorRT deployment pipelines
- Create TensorRT plugins
- Develop concurrent memory safe inference code
- Implement model conversion pipelines
- Optimize large-scale models
- Perform parity checking between frameworks and binaries
- Quantize models for inference
- Recover accuracy after optimization
- Write custom CUDA kernels
Perks/Benefits
- N/A
Skills/Tech-stack
Accuracy evaluation | BF16 | C++ | CUDA | CUDA kernels | Cache optimization | Causal Attention | Compilation Pipeline | Efficient Fine Tuning | FP16 | FP8 | Fine Tuning | FlashAttention | INT4) | INT8 | KV cache | KV cache optimization | Latency benchmarking | Linear-attention | LoRA | Memory Optimization | Mixed Precision | Model Conversion | PagedAttention | Parameter efficient fine-tuning | Post-training | Post-training Quantization | PyTorch | Python | QLoRA | Quantization | Quantization aware training | Speculative decoding | TensorRT | TensorRT Plugins | TensorRT-LLM
Education
N/A
Regions
Countries
States
Cities
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