AI Performance Optimization Engineer
Tasks
- Apply compiler level optimizations with Triton XLA TorchInductor TVM
- Build benchmark suites and regression frameworks
- Collaborate with ML platform teams to standardize performance best practices
- Document performance tuning playbooks and share findings
- Eliminate performance bottlenecks in data loading model compute communication memory
- Evaluate new hardware and software offerings
- Implement quantization pruning sparsity
- Improve AI cost efficiency through scheduling hardware model architecture
- Optimize AI inference pipelines for throughput latency cost
- Optimize AI training pipelines for throughput latency cost
- Optimize KV cache continuous batching speculative decoding
- Optimize attention with FlashAttention paged attention
- Tune distributed training with tensor parallelism pipeline parallelism FSDP ZeRO
Perks/Benefits
Skills/Tech-stack
Attention Mechanisms | Benchmarking | C++ | Continuous batching | Data pipeline | Data pipeline optimization | Deep learning | Distributed Training | FSDP | FlashAttention | GPU Architecture | KV cache | Memory Management | Model Compression | Paged Attention | Pipeline Optimization | Pipeline parallelism | Profiling | Pruning | Python | Quantization | Regression testing | Sparsity | Speculative decoding | TVM | Tensor Parallelism | TorchInductor | Triton | XLA | Zero
Education
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