AI Performance Optimization Engineer
Tasks
- Build and maintain benchmark and regression suites
- Collaborate with ML and platform engineering teams
- Drive compiler level optimization for measurable gains
- Evaluate new hardware and software offerings
- Identify and eliminate performance bottlenecks
- Implement and tune quantization sparsity and pruning
- Improve cost efficiency through hardware scheduling and architecture
- Optimize KV cache continuous batching and speculative decoding
- Optimize data pipelines and storage access patterns
- Optimize distributed training with parallelism and sharding
- Profile and optimize AI training and inference pipelines
- Tune attention implementations for performance
Perks/Benefits
Skills/Tech-stack
Benchmarking | C++ | CUDA | Continuous batching | Deep learning | Distributed Training | FSDP | FlashAttention | GPU Architecture | Inference Optimization | KV cache | Paged Attention | Pipeline parallelism | Profiling tools | Pruning | Python | Quantization | Sparsity | Speculative decoding | TVM | Tensor Parallelism | TorchInductor | Triton | XLA | Zero
Education
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