AI Performance Optimization Engineer
Tasks
- Build and maintain benchmark suites
- Build and maintain regression frameworks
- Collaborate with ML and platform engineering teams
- Document performance tuning playbooks
- Drive compiler level optimizations with Triton or XLA or TorchInductor or TVM
- Eliminate bottlenecks in data loading model compute communication and memory
- Evaluate new hardware and software offerings
- Implement speculative decoding
- Improve cost efficiency with model and hardware optimization
- Minimize latency and throughput issues
- Optimize KV cache for LLM serving
- Optimize continuous batching
- Optimize distributed training with FSDP
- Optimize distributed training with ZeRO style sharding
- Optimize distributed training with tensor parallelism and pipeline parallelism
- Profile end to end AI inference pipelines
- Profile end to end AI training pipelines
- Translate AI research into production improvements
- Tune attention implementations with FlashAttention
- Tune quantization sparsity and pruning strategies
Perks/Benefits
- N/A
Skills/Tech-stack
Access Optimization | Attention Mechanisms | Benchmarking | C++ | Communication Primitives | Continuous batching | Data Sharding | Data loading | Deep learning | Distributed Training | Distributed inference | FSDP | FlashAttention | GPU | KV cache | Memory Management | Model Parallelism | Paged Attention | Pipeline parallelism | Profiling tools | Pruning | Python | Quantization | Regression testing | Sparsity | Speculative decoding | Storage Access | Storage Access Optimization | TVM | Tensor Parallelism | Torch Inductor | TorchInductor | Triton | XLA | Zero
Education
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