Model Optimization Engineer
Tasks
- Build benchmark suites and regression frameworks
- Collaborate with ML and platform teams to standardize pipelines
- Document performance tuning playbooks
- Drive compiler level optimizations with ML compilers
- Eliminate bottlenecks in data loading model compute communication memory
- Evaluate new hardware and software offerings
- Implement KV cache optimization continuous batching speculative decoding
- Improve cost efficiency through architecture hardware scheduling
- Optimize AI training and inference pipelines for throughput latency cost
- Optimize attention implementations for inference speed
- Optimize distributed training with parallelism and sharding
- Tune quantization sparsity pruning for faster inference
Perks/Benefits
- N/A
Skills/Tech-stack
Benchmarking | C++ | CUDA | Continuous batching | Cutlass | DeepSpeed | Distributed Training | FSDP | FinOps | FlashAttention | GPU Architecture | KV cache | Memory Management | Model Parallelism | Paged Attention | Pipeline parallelism | Profiling | Pruning | Python | Quantization | Sparsity | Speculative decoding | TVM | Tensor Parallelism | TensorRT-LLM | TorchInductor | Triton | VLLM | XLA | Zero
Education
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