AI Research Engineer (Kernel & Inference Optimization)
Tasks
- Build and monitor inference tests
- Collaborate with cross-functional teams
- Design model serving architectures
- Develop inference algorithms
- Diagnose serving bottlenecks
- Establish performance metrics
- Integrate serving frameworks into production
- Optimize batch processing
- Optimize inference pipelines
- Optimize memory usage
- Prepare test datasets and simulation scenarios
Perks/Benefits
Skills/Tech-stack
Compute Shaders | Diffusion Models | Distributed Inference Systems | Distributed inference | Edge Computing | Embedded inference | Expert parallelism | Flash Attention | GPU Kernels | High Throughput | Inference Optimization | Inference Systems | KV cache | Low Latency | Machine Learning | Memory Optimization | Metal Shading Language | Mobile optimization | Model Serving | NLP | Pipeline parallelism | Pruning | Python | Quantization | Shading language | Speculative decoding | Tensor Parallelism | Tensor Processing Units | Tensor processing | Vision Transformers
Education
Roles
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