AI Performance Optimization Engineer
Tasks
- Build and maintain benchmark suites and regression frameworks
- Collaborate with ML and platform teams to embed best practices
- Document performance tuning playbooks and share findings
- Drive compiler level optimizations using Triton XLA Torch Inductor and TVM
- Evaluate hardware and software offerings and advise on adoption
- Identify and eliminate bottlenecks in data loading model compute communication and memory
- Implement and tune quantization sparsity and pruning for faster inference
- Optimize KV cache continuous batching and speculative decoding for LLM serving
- Optimize data pipelines sharding strategies and storage access patterns
- Optimize distributed training using tensor parallelism pipeline parallelism FSDP and ZeRO style sharding
- Profile AI training and inference pipelines for throughput latency and cost
- Tune attention implementations using Flash Attention and paged attention
Perks/Benefits
Skills/Tech-stack
Batching | Benchmarking | C++ | CUDA | Compiler optimization | Deep learning | DeepSpeed | Distributed Training | FSDP | Flash Attention | GPU | KV cache | LLM | LLM Inference | Machine Learning | Memory Management | Model Pruning | Model Quantization | Model Sparsity | Paged Attention | Pipeline parallelism | Profiling | Python | Regression testing | Speculative decoding | TVM | Tensor Parallelism | TensorRT-LLM | Torch | Torch Inductor | Triton | VLLM | XLA | Zero
Education
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