AI Performance Optimization Engineer
Tasks
- Apply compiler level optimizations for end to end performance
- Build benchmark suites and regression frameworks
- Collaborate with ML and platform teams to standardize best practices
- Document performance tuning playbooks and share findings
- Evaluate new hardware and software offerings
- Identify and eliminate performance bottlenecks
- Implement and tune quantization sparsity and pruning
- Improve cost efficiency through architecture hardware and scheduling
- Optimize AI training and inference pipelines for throughput latency and cost
- Optimize KV cache and batching for LLM serving
- Optimize distributed training with parallelism and sharding
- Translate AI research into production performance improvements
- Tune attention implementations for inference efficiency
Perks/Benefits
Skills/Tech-stack
Attention Mechanisms | C++ | Compiler optimization | Continuous batching | Custom Kernel | Custom Kernel Authoring | Cutlass | DeepSpeed | Distributed Training | FSDP | FinOps | FlashAttention | GPU Architecture | HPC | KV cache | Kernel authoring | Memory Management | Model Parallelism | Paged Attention | Pipeline parallelism | Profiling | Pruning | Python | Quantization | Sparsity | Speculative decoding | TVM | Tensor Parallelism | TensorRT-LLM | TorchInductor | Triton | VLLM | XLA | Zero
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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