ML Performance Engineer
Tasks
- Build benchmark suites and regression frameworks
- Collaborate with ML platform teams to embed best practices
- Document performance tuning playbooks and share findings
- Drive compiler level optimizations with performance kernels
- Evaluate new hardware and software and advise adoption
- Identify and eliminate bottlenecks in data loading model compute communication memory
- Implement and tune quantization sparsity pruning
- Improve AI cost efficiency with scheduling and architecture changes
- Optimize KV cache continuous batching speculative decoding for LLM serving
- Optimize data pipeline sharding and storage access patterns
- Optimize distributed training with parallelism and sharding
- Profile and optimize AI training pipelines for throughput latency cost
- Tune attention implementations for faster inference
Perks/Benefits
Skills/Tech-stack
Benchmarking | C++ | Continuous batching | Cutlass | Deep learning | DeepSpeed | Distributed Training | FSDP | FlashAttention | GPU Architecture | KV cache | LLM Inference | 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
N/A
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