AI Performance Optimization Engineer
Tasks
- Build benchmark and regression suites
- Document performance tuning playbooks
- Evaluate new hardware and software offerings
- Identify and eliminate performance bottlenecks
- Improve cost efficiency for AI workloads
- Optimize AI inference pipelines
- Optimize AI training pipelines
- Optimize KV cache and batching
- Optimize attention implementations
- Optimize compiler level performance
- Optimize distributed training
- Profile and measure CPU GPU and distributed workloads
- Tune model quantization sparsity and pruning
Perks/Benefits
- N/A
Skills/Tech-stack
Benchmarking | C++ | CUDA | Communication Primitives | Continuous batching | DeepSpeed | Distributed Training | FSDP | FinOps | Flash Attention | GPU | KV cache | LLM Inference | Memory Management | Model Compression | Model Parallelism | Paged Attention | Pipeline parallelism | Profiling | Pruning | Python | Quantization | Sparsity | Speculative decoding | TVM | TensorRT | Torch Inductor | Triton | XLA | Zero
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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