AI Performance Optimization Engineer
Tasks
- Build benchmark suites and regression frameworks
- Collaborate with ML and platform teams on best practices
- Document performance tuning playbooks
- Drive compiler level optimizations for performance
- Eliminate bottlenecks in data loading compute communication and memory
- Evaluate new hardware and software offerings
- Implement quantization pruning and sparsity
- Improve cost efficiency through scheduling and hardware selection
- Optimize KV cache continuous batching and speculative decoding
- Optimize attention implementations
- Optimize throughput and latency
- Profile AI training and inference pipelines
- Tune distributed training with parallelism sharding and FSDP
Perks/Benefits
Skills/Tech-stack
C++ | CUDA | Continuous batching | DeepSpeed | Distributed Training | FSDP | Flash Attention | GPU Architecture | Inference Optimization | KV cache | Memory Management | Model Compression | Paged Attention | Pipeline parallelism | Profiling | Pruning | Python | Quantization | Sparsity | Speculative decoding | TVM | Tensor Parallelism | Torch Inductor | Triton | XLA | Zero
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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