AI Performance Optimization Engineer
United States - Remote
R
USD 136K-258K (estimate) Mid-level Full Time
Tasks
- Build benchmarking and regression frameworks
- Collaborate on ML systems best practices
- Document performance tuning playbooks
- Drive compiler level optimizations
- Evaluate hardware and software for adoption
- Identify and eliminate performance bottlenecks
- Implement quantization sparsity and pruning
- Improve AI cost efficiency
- Optimize KV cache and batching for LLM serving
- Optimize data pipelines and storage access
- Optimize distributed training with parallelism
- Profile and optimize AI training and inference pipelines
- Stay current with AI performance research
- Tune attention implementations
Perks/Benefits
Skills/Tech-stack
Access patterns | Benchmarking | C++ | Cache optimization | Compiler optimization | Continuous batching | Data Sharding | Deep learning | Distributed Training | Distributed inference | FSDP | FlashAttention | GPU Architecture | KV cache | KV cache optimization | Memory Management | Model Parallelism | Paged Attention | Pipeline parallelism | Profiling tools | Pruning | Python | Quantization | Regression testing | Sparsity | Speculative decoding | Storage Access | Storage Access Patterns | TVM | Tensor Parallelism | TorchInductor | Triton | XLA | Zero
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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