ML Performance Engineer
Tasks
- Build benchmark suites and regression frameworks
- Collaborate with ML platform teams on best practices
- Document performance tuning playbooks
- Evaluate new hardware and software
- Identify and eliminate performance bottlenecks
- Implement and tune quantization sparsity and pruning
- Implement compiler-level optimizations
- Improve cost efficiency for AI workloads
- Optimize AI training and inference pipelines
- Optimize KV cache and batching for LLM serving
- Optimize data pipelines and storage access
- Optimize distributed training parallelism
- Translate AI research into production improvements
- Tune attention implementations
Perks/Benefits
Skills/Tech-stack
Benchmarking | C++ | Compiler optimization | Continuous batching | Custom Kernel | Custom kernel development | Cutlass | Data-parallel | Deep learning | DeepSpeed | Distributed Training | FinOps | FlashAttention | Fully Sharded Data Parallel | GPU Architecture | KV cache | Kernel development | Memory Management | Model Parallelism | Paged Attention | Pipeline parallelism | Profiling | Pruning | Python | Quantization | Regression testing | Sparsity | Speculative decoding | TVM | Tensor Parallelism | TensorRT-LLM | TorchInductor | Triton | VLLM | XLA | Zero
Education
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