AI Performance Optimization Engineer
Tasks
- Build benchmark and regression frameworks
- Collaborate with ML and platform engineering teams
- Document performance tuning playbooks
- Drive cost efficiency improvements
- Evaluate new hardware and software
- Identify and eliminate bottlenecks
- Implement compiler-level optimizations
- Implement quantization sparsity and pruning
- Optimize KV cache and batching
- Optimize data pipelines and storage access
- Optimize distributed training
- Profile and optimize AI training and inference pipelines
- Stay current with AI systems research
- Tune attention implementations
Perks/Benefits
Skills/Tech-stack
Benchmarking | C++ | Continuous batching | Deep learning | DeepSpeed | Distributed Training | FSDP | FinOps | FlashAttention | GPU Architecture | KV cache | Memory Management | Model Compression | Model Parallelism | PagedAttention | Pipeline parallelism | Profiling | Pruning | Python | Quantization | Regression testing | Sparsity | Speculative decoding | TVM | Tensor Parallelism | TensorRT-LLM | TorchInductor | Triton | VLLM | XLA | Zero
Education
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