AI Performance Optimization Engineer
Tasks
- Build benchmark suites and regression frameworks
- Collaborate with ML and platform engineering teams to embed best practices
- Document performance tuning playbooks
- Drive compiler level optimizations with ML compilers
- Evaluate new hardware and software offerings
- Identify and eliminate bottlenecks in data loading model compute communication and memory
- Implement and tune quantization sparsity and pruning
- Improve cost efficiency through architecture hardware selection and scheduling
- Optimize AI training and inference pipelines for throughput latency and cost
- Optimize KV cache and batching strategies for LLM serving
- Optimize data pipelines sharding and storage access patterns
- Optimize distributed training with model parallelism and sharding
- Tune attention implementations for performance
Perks/Benefits
Skills/Tech-stack
C++ | CPU Profiling | Continuous batching | Cutlass | Deep Learning Profiling | Deep learning | DeepSpeed | Distributed Training | Distributed inference | FSDP | FinOps | FlashAttention | GPU Architecture | GPU Profiling | KV cache | Memory Management | Model Parallelism | Paged Attention | Pipeline parallelism | Pruning | Python | Quantization | Sparsity | Speculative decoding | TVM | Tensor Parallelism | TensorRT | TensorRT-LLM | TorchInductor | Triton | VLLM | XLA | Zero
Education
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